Update README.md

README.md

@@ -1,3 +1,127 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+- financial
+- stocks
+- sentiment
+- sentiment-analysis
+- financial-news
+widget:
+- text: The company's quarterly earnings surpassed all estimates, indicating strong growth.
+datasets:
+- financial_phrasebank
+metrics:
+- accuracy
+model-index:
+- name: AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: financial_phrasebank
+      type: financial_phrasebank
+      args: sentences_allagree
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.96688
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased-finetuned-sst-2-english
+pipeline_tag: text-classification
+library_name: transformers
+---
+# DistilBERT Fine-Tuned for Financial Sentiment Analysis
+## Model Description
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) specifically tailored for sentiment analysis in the financial domain. It has been trained on the [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank) dataset to classify financial texts into three sentiment categories:
+
+- Negative (label `0`)
+- Neutral (label `1`)
+- Positive (label `2`)
+  
+## Model Performance
+The model was trained for 5 epochs and evaluated on a held-out test set constituting 20 of the dataset.
+
+### Evaluation Metrics
+| Epoch | Eval Loss | Eval Accuracy |
+|-----------|---------------|-------------------|
+| 1         | 0.2210        | 94.26%            |
+| 2         | 0.1997        | 95.81%            |
+| 3         | 0.1719        | 96.69%            |
+| 4         | 0.2073        | 96.03%            |
+| 5         | 0.1941        | **96.69%**        |
+
+Final Evaluation Accuracy**: **96.69%**
+
+### Training Metrics
+- **Final Training Loss**: 0.0797
+- **Total Training Time**: Approximately 3869 seconds (~1.07 hours)
+- **Training Samples per Second**: 2.34
+- **Training Steps per Second**: 0.147
+
+## Training Procedure
+### Data
+- **Dataset**: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)
+- **Configuration**: `sentences_allagree` (sentences where all annotators agreed on the sentiment)
+- **Dataset Size**: 2264 sentences
+- **Data Split**: 80% training (1811 samples), 20% testing (453 samples)
+
+### Model Configuration
+- **Base Model**: [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)
+- **Number of Labels**: 3 (negative, neutral, positive)
+- **Tokenizer**: Same as the base model's tokenizer
+
+### Hyperparameters
+- **Number of Epochs**: 5
+- **Batch Size**: 16 (training), 64 (evaluation)
+- **Learning Rate**: 5e-5
+- **Optimizer**: AdamW
+- **Evaluation Metric**: Accuracy
+- **Seed**: 42 (for reproducibility)
+
+## Usage
+You can load and use the model with the Hugging Face `transformers` library as follows:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained('AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis')
+model = AutoModelForSequenceClassification.from_pretrained('AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis')
+
+text = "The company's quarterly earnings surpassed all estimates, indicating strong growth."
+inputs = tokenizer(text, return_tensors="pt")
+
+outputs = model(**inputs)
+predictions = outputs.logits.argmax(dim=-1)
+
+label_mapping = {0: 'Negative', 1: 'Neutral', 2: 'Positive'}
+print(f"Sentiment: {label_mapping[predictions.item()]}")
+```
+
+## License
+This model is licensed under the **Apache 2.0 License**. You are free to use, modify, and distribute this model in your applications.
+
+## Citation
+If you use this model in your research or applications, please cite it as:
+```
+@misc{AnkitAI_2024_financial_sentiment_model,
+  title={DistilBERT Fine-Tuned for Financial Sentiment Analysis},
+  author={Ankit Aglawe},
+  year={2024},
+  howpublished={\url{https://huggingface.co/AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis}},
+}
+```
+## Acknowledgments
+- **Hugging Face**: For providing the Transformers library and model hosting.
+- **Data Providers**: Thanks to the creators of the Financial PhraseBank dataset.
+- **Community**: Appreciation to the open-source community for continual support and contributions.
+
+## Contact Information
+For questions, feedback, or collaboration opportunities, please contact:
+- **Name**: Ankit Aglawe
+- **Email**: [[email protected]]
+- **GitHub**: [GitHub Profile](https://github.com/ankit-aglawe)
+- **LinkedIn**: [LinkedIn Profile](https://www.linkedin.com/in/ankit-aglawe)