| --- |
| library_name: transformers |
| datasets: |
| - mteb/tweet_sentiment_extraction |
| base_model: |
| - openai-community/gpt2 |
| --- |
| |
| # Model Card for Model ID |
|
|
| This model fine-tunes GPT-2 on the "Tweet Sentiment Extraction" dataset for sentiment analysis tasks. |
|
|
|
|
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
|
|
| This model fine-tunes GPT-2 using the "Tweet Sentiment Extraction" dataset to extract sentiment-relevant portions of text. |
| It demonstrates preprocessing, tokenization, and fine-tuning with Hugging Face libraries. |
|
|
|
|
|
|
| ## Uses |
|
|
|
|
|
|
| ### Direct Use |
|
|
| This model can be used to analyze text for sentiment-relevant extractions directly after fine-tuning. |
| It works as a baseline model for learning sentiment-specific features. |
|
|
| ### Downstream Use [optional] |
|
|
| Fine-tuned for tasks that involve sentiment analysis, such as social media monitoring or customer feedback analysis. |
|
|
|
|
| ### Out-of-Scope Use |
|
|
| Avoid using the model for real-time sentiment prediction or deployment without additional training/testing for specific use cases. |
|
|
|
|
| ## Bias, Risks, and Limitations |
|
|
| The dataset used may not fully represent the diversity of text, leading to biases in the output. There is a risk of overfitting to the specific dataset. |
|
|
| ### Recommendations |
|
|
| Carefully evaluate the model for biases and limitations before deploying in production environments. Consider retraining on a more diverse dataset if required. |
|
|
|
|
| ## How to Get Started with the Model |
|
|
| Use the code below to get started with the model. |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("https://huggingface.co/Wexnflex/Tweet_Sentiment") |
| tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/Wexnflex/Tweet_Sentiment") |
| |
| text = "Input your text here." |
| inputs = tokenizer(text, return_tensors="pt") |
| outputs = model.generate(**inputs) |
| print(tokenizer.decode(outputs[0])) |
| |
| |
| |
| #### Training Hyperparameters |
| |
| Training Hyperparameters |
| Batch size: 16 |
| Learning rate: 2e-5 |
| Epochs: 3 |
| Optimizer: AdamW |
| |
| #### Testing Data, Factors & Metrics |
| |
| #### Testing Data |
| |
| The evaluation was performed on the test split of the "Tweet Sentiment Extraction" dataset. |
| |
| |
| #### Factors |
| |
| Evaluation is segmented by sentiment labels (e.g., positive, negative, neutral). |
| |
| |
| #### Metrics |
| |
| Accuracy |
| |
| ### Results |
| |
| 70% Accuracy |
| #### Summary |
| |
| The fine-tuned model performs well for extracting sentiment-relevant text, with room for improvement in handling ambiguous cases. |
| |
| |
| ## Technical Specifications [optional] |
| |
| |
| #### Hardware |
| |
| T4 GPU (Google Colab) |
| #### Software |
| |
| Hugging Face Transformers Library |
| |
| |
