Custom-BERT-NER-Model

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AventIQ-AI

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Quick Summary

This repository contains a BERT-based Named Entity Recognition (NER) model fine-tuned on the CoNLL-2003 dataset.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Custom-BERT-NER-Model with quality assessment

Specialized For

general

multilingual

Training Datasets (1)

🔵 6/10

general

multilingual

Key Strengths

•Scale and Accessibility: 750GB of publicly available, filtered text
•Systematic Filtering: Documented heuristics enable reproducibility
•Language Diversity: Despite English-only, captures diverse writing styles

Considerations

•English-Only: Limits multilingual applications
•Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

Installationpyhtontransformers

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"

tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
model = BertForTokenClassification.from_pretrained(model_name_or_path)

model.to("cuda")  # or "cpu"
model.eval()

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