Updated the Model card

README.md

@@ -17,20 +17,39 @@ This model is a fine-tuned version of [openai-community/gpt2](https://huggingfac
 It achieves the following results on the evaluation set:
 - Loss: 3.3924
 
-## Model description
+## Model Description
 
-More information needed
+This language model is built on the GPT-2 architecture provided by OpenAI. The tokenizer utilized for preprocessing text data is OpenAI's tikToken. For more details on tikToken, you can refer to the [official GitHub repository](https://github.com/openai/tiktoken).
 
-## Intended uses & limitations
+### Tokenizer Overview
 
-More information needed
+To interactively explore the functionality and behavior of the tikToken tokenizer, you can use the [tikToken interactive website](https://tiktokenizer.vercel.app/). This website allows you to quickly visualize the tokenization process and understand how the tokenizer segments input text into tokens.
 
+### Model Checkpoint
+
+The model checkpoint used in this implementation is sourced from the OpenAI community and is based on the GPT-2 architecture. You can find the specific model checkpoint at the following Hugging Face Model Hub link: [openai-community/gpt2](https://huggingface.co/openai-community/gpt2).
+
+### Training Details
+
+The model was trained for a total of 3 epochs on the provided dataset. This information reflects the number of times the entire training dataset was processed during the training phase. Training for a specific number of epochs helps control the duration and scope of the model's learning process.
+
+ 
 ## Training and evaluation data
+  
+#### Evaluation Data
+
+For evaluating the model's performance, the training script utilized an evaluation dataset. 
+
+#### Evaluation Results
 
-More information needed
+After training, the model's performance was assessed using the evaluation dataset. The perplexity, a common metric for language modeling tasks was **Perplexity: 29.74**
 
-## Training procedure
+```python
+eval_results = trainer.evaluate()
+print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
 
+>>> Perplexity : 29.74
+``` 
 ### Training hyperparameters
 
 The following hyperparameters were used during training: