Update README.md

README.md

@@ -1,3 +1,102 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- neural-bridge/rag-dataset-12000
+- neural-bridge/rag-dataset-1200
+language:
+- en
+---
+
+# RAGPT-2: Fine-tuned GPT-2 for Context-Based Question Answering
+
+## Model Description
+
+RAGPT-2 is a fine-tuned version of [GPT-2 small](https://huggingface.co/BueormLLC/CleanGPT), specifically adapted for context-based question answering tasks. This model has been trained to generate relevant answers based on a given context and question, similar to a Retrieval-Augmented Generation (RAG) system.
+
+### Key Features
+
+- Based on the GPT-2 small architecture (124M parameters)
+- Fine-tuned on the "neural-bridge/rag-dataset-12000" and others dataset from Hugging Face
+- Capable of generating answers based on provided context and questions
+- Suitable for various question-answering applications
+
+## Training Data
+
+The model was fine-tuned using the "neural-bridge/rag-dataset-12000" and "neural-bridge/rag-dataset-1200" dataset, which contains:
+- Context passages
+- Questions related to the context
+- Corresponding answers
+
+## Fine-tuning Process
+
+The fine-tuning process involved:
+1. Loading the pre-trained GPT-2 small model
+2. Preprocessing the dataset to combine context, question, and answer into a single text
+3. Training the model to predict the next token given the context and question
+
+### Hyperparameters
+
+- Base model: GPT-2 small
+- Number of training epochs: 8
+- Batch size: 4
+- Learning rate: Default AdamW optimizer settings
+- Max sequence length: 512 tokens
+
+## Usage
+
+To use the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "BueormLLC/RAGPT-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Prepare input
+context = "Your context here"
+question = "Your question here"
+input_text = f"Contexto: {context}\nPregunta: {question}\nRespuesta:"
+
+# Generate answer
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+output = model.generate(input_ids, max_length=150, num_return_sequences=1)
+answer = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
+## Limitations
+
+- The model's knowledge is limited to its training data and the base GPT-2 model.
+- It may sometimes generate irrelevant or incorrect answers, especially for topics outside its training domain.
+- The model does not have access to external information or real-time data.
+
+## Ethical Considerations
+
+Users should be aware that this model, like all language models, may reflect biases present in its training data. It should not be used as a sole source of information for critical decisions.
+
+## Future Improvements
+
+- Fine-tuning on a larger and more diverse dataset
+- Experimenting with larger base models (e.g., GPT-2 medium or large)
+- Implementing techniques to improve factual accuracy and reduce hallucinations
+
+## Support us
+
+- [Paypal](https://paypal.me/bueorm)
+- [Patreon](https://patreon.com/bueorm)
+### We appreciate your support, without you we could not do what we do.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@misc{RAGPT,
+  author = {Bueorm},
+  title = {RAGPT-2: Fine-tuned GPT-2 for Context-Based Question Answering},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {None},
+  howpublished = {\url{https://huggingface.co/BueormLLC/RAGPT-2}}
+}
+```