roberta-based-sentiment-analysis-for-twitter-tweets

Name: roberta-based-sentiment-analysis-for-twitter-tweets
Author: AventIQ-AI

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Quick Summary

RoBERTa-Base Quantized Model for Sentiment Analysis This repository hosts a quantized version of the RoBERTa model, fine-tuned for sentiment-analysis-twitter-tweets.

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

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Usagepythontransformers

from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Define a test sentence

test_sentence = "The food was absolutely delicious and the service was amazing!"
 
# Tokenize input

inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
# Ensure input tensors are in correct dtype

inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type

inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

 
# Make prediction

with torch.no_grad():

    outputs = quantized_model(**inputs)
 
# Get predicted class

predicted_class = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted Class: {predicted_class}")
 
 
label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}   

#Example
 
predicted_label = label_mapping[predicted_class]

print(f"Predicted Label: {predicted_label}")

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

Quantizationtext

.
├── config.json
├── tokenizer_config.json    
├── special_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

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