distilbert-base-uncased-sentiment-analysis

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Quick Summary

DistilBERT Base Uncased Quantized Model for Sentiment Analysis This repository hosts a quantized version of the DistilBERT model, fine-tuned for sentiment analysis tasks.

Code Examples

Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagebash
pip install transformers torch
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Usagepythontransformers
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    return "Positive" if predicted_class_id == 1 else "Negative"

# Test the model with a sample sentence
test_text = "I absolutely loved the movie! It was fantastic."
print(f"Sentiment: {predict_sentiment(test_text)}")
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation
Quantizationtext
.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation

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