LayoutLMv1_Information_Extraction

Name: LayoutLMv1_Information_Extraction
Author: AventIQ-AI

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AventIQ-AI

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Quick Summary

AI model with specialized capabilities.

Code Examples

Usagebash

pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

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pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagebash

pip install transformers datasets torch torchvision

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Usagepythontransformers

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Quantizationpythonpytorch

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Repository Structuretext

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

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