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
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.
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