README.md

metadata

library_name: transformers
tags:
  - sentiment-analysis
  - twitter
  - roberta
  - text-classification
datasets:
  - tweet_eval

Sentiment Analysis Model

Model Details

Model Description

Fine-tuned RoBERTa model for sentiment analysis on tweets, trained on the TweetEval benchmark.

• Developed by: Your Name
• Model type: RoBERTa (Sequence Classification)
• Language: English
• License: MIT
• Finetuned from: cardiffnlp/twitter-roberta-base-sentiment-latest

Model Sources

• Repository: HuggingFace Hub
• Paper: TweetEval

Uses

Direct Use

This model can be used for sentiment analysis on English tweets. It classifies text into three categories:

• negative
• neutral
• positive

Out-of-Scope Use

• Non-English text
• Very long documents (>512 tokens)
• Non-tweet content (may have reduced accuracy)

Bias, Risks, and Limitations

• Model trained on English tweets only
• May not generalize well to other domains
• Sentiment labels may not capture nuance in complex expressions
• Sarcasm and irony may be misclassified

Recommendations

Users should be aware that this model is specifically trained on tweets and may not perform well on other types of text. For production use, consider fine-tuning on domain-specific data.

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="giovannibonisoli/sentiment-model")
result = classifier("I love this!")
# [{'label': 'positive', 'score': 0.98}]

Training Details

Training Data

• Dataset: TweetEval (sentiment)
• Train split: 1000 samples (configurable via TRAIN_SAMPLES env var)
• Validation split: 1000 samples (configurable via VALIDATION_SAMPLES env var)

Training Procedure

Preprocessing

• Tokenization with truncation at 512 tokens
• Padding to max_length
• Batched processing

Training Hyperparameters

• Training regime: fp16 mixed precision
• Epochs: 3 (configurable via NUM_EPOCHS)
• Batch size: 16
• Optimizer: AdamW (default)
• Learning rate: 2e-5 (transformers default)

Training Configuration

TrainingArguments(
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    eval_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
    metric_for_best_model="macro_f1",
    logging_steps=50
)

Evaluation

Testing Data

• Dataset: TweetEval (sentiment) - test split

Metrics

• Accuracy: ~0.72
• Macro F1: ~0.72

Results

Final metrics after training:

• Accuracy: {accuracy_value_from_CI}
• Macro F1: {f1_value_from_CI}

Environmental Impact

• Hardware Type: GPU (GitHub Actions runner)
• Hours used: ~5 minutes
• Cloud Provider: GitHub Actions

Technical Specifications

Model Architecture and Objective

• Base model: RoBERTa-base
• Objective: Sequence Classification (3 classes)
• Max sequence length: 512 tokens
• Parameters: ~125M

Compute Infrastructure

• Training: GitHub Actions (Ubuntu runner with GPU)
• Storage: HuggingFace Hub