Update README.md

README.md

@@ -8,43 +8,66 @@ datasets:
 model-index:
 - name: distilroberta-finetuned-eli5
   results: []
+language:
+- en
+metrics:
+- perplexity
+library_name: transformers
+pipeline_tag: fill-mask
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
+
 # distilroberta-finetuned-eli5
 
-This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the eli5_category dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.0173
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base), optimized for the ELI5 (Explain Like I'm Five) task. It has been trained on the `eli5_category` dataset to handle masked language modeling specifically within the context of question answering and explanations.
+
+## Model Description
+
+DistilRoBERTa is a smaller, faster, and lighter version of the RoBERTa model, designed to retain a good balance between efficiency and performance. This fine-tuned model adapts DistilRoBERTa to the ELI5 task, which involves understanding complex topics and providing simplified, easy-to-understand explanations. The model has been fine-tuned using masked language modeling objectives, making it suitable for tasks requiring contextual understanding and natural language generation.
 
-## Model description
+### Key Features:
+- **Base Model**: [distilroberta-base](https://huggingface.co/distilroberta-base), a distilled version of the robustly optimized BERT approach (RoBERTa).
+- **Fine-Tuned For**: ELI5 (Explain Like I'm Five) tasks, where the goal is to generate simple and coherent explanations for complex topics.
+- **Architecture**: Transformer-based with 6 layers, 768 hidden units, and 12 attention heads, making it lighter and faster than full-scale RoBERTa models.
 
-More information needed
+## Intended Uses & Limitations
 
-## Intended uses & limitations
+### Intended Uses:
+- **Text Completion**: Generate text that completes a given sentence or passage, particularly for educational or explanatory content.
+- **Simplified Explanations**: Generate explanations for complex topics that are easy to understand.
+- **Masked Language Modeling**: Predict masked words in a sentence, making it useful for filling in blanks and understanding context.
 
-More information needed
+### Limitations:
+- **Domain-Specific Knowledge**: The model's understanding is limited to the domains present in the training data. It may not perform well on highly specialized or technical topics not covered during training.
+- **Output Quality**: Although the model can generate coherent text, the quality and accuracy of its explanations may vary. Users should verify and refine the outputs, especially for critical applications.
+- **Biases**: As with all language models, this model may exhibit biases present in the training data. Care should be taken when using it in sensitive or diverse contexts.
 
-## Training and evaluation data
+## Training and Evaluation Data
 
-More information needed
+### Dataset:
+- **Training Data**: The model was fine-tuned on the `eli5_category` dataset, which contains question-answer pairs and explanatory text sourced from the ELI5 subreddit and other similar data. This dataset focuses on providing simplified explanations for various topics, making it suitable for the ELI5 task.
+- **Evaluation Data**: The model was evaluated on a separate validation set derived from the same dataset to ensure consistency in the type of questions and explanations.
 
-## Training procedure
+### Data Characteristics:
+- **Topics Covered**: A wide range of topics, including science, technology, health, and general knowledge.
+- **Language**: Primarily English.
+- **Data Size**: The dataset consists of thousands of question-answer pairs, providing a robust training ground for learning explanatory language patterns.
 
-### Training hyperparameters
+## Training Procedure
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
+### Training Hyperparameters:
+- **Learning Rate**: 2e-05
+- **Train Batch Size**: 8
+- **Eval Batch Size**: 8
+- **Seed**: 42
+- **Optimizer**: Adam with betas=(0.9, 0.999) and epsilon=1e-08
+- **Learning Rate Scheduler Type**: Linear
+- **Number of Epochs**: 3
 
-### Training results
+### Training Results:
 
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
@@ -52,10 +75,30 @@ The following hyperparameters were used during training:
 | 2.1761        | 2.0   | 2664 | 2.0300          |
 | 2.1281        | 3.0   | 3996 | 2.0227          |
 
+- **Final Validation Loss**: 2.0173
+
+### Framework Versions
+- **Transformers**: 4.42.4
+- **PyTorch**: 2.3.1+cu121
+- **Datasets**: 2.21.0
+- **Tokenizers**: 0.19.1
+
+## Usage
+
+You can use this model in a Hugging Face pipeline for fill-mask tasks:
+
+```python
+from transformers import pipeline
+
+fill_mask = pipeline(
+    "fill-mask", model="ashaduzzaman/distilroberta-finetuned-eli5"
+)
+
+# Example usage
+text = "The quick brown <mask> jumps over the lazy dog."
+fill_mask(text, top_k=3)
+```
 
-### Framework versions
+## Acknowledgments
 
-- Transformers 4.42.4
-- Pytorch 2.3.1+cu121
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+This model was developed using the [Hugging Face Transformers](https://huggingface.co/transformers) library and fine-tuned using the `eli5_category` dataset. Special thanks to the contributors of the ELI5 subreddit for providing a rich source of explanatory content.