Update README.md

README.md

@@ -17,10 +17,10 @@ bert_squad is a transformer-based model trained for context-based question answe
 
 The model was trained using free computational resources, demonstrating its accessibility for educational and small-scale research purposes.
 
-- **Developed by: SADAT PARVEJ, RAFIFA BINTE JAHIR
-- **Shared by [optional]: SADAT PARVEJ
-- **Language(s) (NLP): ENGLISH
-- **Finetuned from model [optional]:https://huggingface.co/google-bert/bert-base-uncased
+Developed by: SADAT PARVEJ, RAFIFA BINTE JAHIR
+Shared by: SADAT PARVEJ
+Language(s) (NLP): ENGLISH
+Finetuned from model: https://huggingface.co/google-bert/bert-base-uncased
 
 ### Model Sources [optional]
 
@@ -30,167 +30,156 @@ The model was trained using free computational resources, demonstrating its acce
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
-## Uses
+## Training Objective
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model predicts the most relevant span of text in a given passage that answers a specific question. It fine-tunes BERT's ability to analyze context using supervised data from SQuAD.
 
-### Direct Use
+### Performance Benchmarks
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Training Loss: 0.477800
+Validation Loss: 0.465936
+Exact Match (EM): 87.568590%
 
-[More Information Needed]
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## Intended Uses & Limitations
 
-[More Information Needed]
+This model is designed for tasks such as:
 
-### Out-of-Scope Use
+Extractive Question Answering
+Reading comprehension applications
+Known Limitations:
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+As BERT is inherently a masked language model (MLM), its original pretraining limits its ability for generative tasks or handling queries outside the SQuAD-style question-answering setup.
+The model's predictions may be biased or overly reliant on the training dataset, as SQuAD comprises structured and fact-based question-answer pairs.
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import pipeline  
+qa_pipeline = pipeline('question-answering', model='bert_squad')  
 
-## Training Details
+context = "BERT is a transformers model for natural language processing."  
+question = "What is BERT used for?"  
+result = qa_pipeline(question=question, context=context)  
+print(result)  
 
-### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+## Training Details
 
-[More Information Needed]
 
-### Training Procedure
+| Step | Training Loss | Validation Loss | Exact Match | Squad F1 | Start Accuracy | End Accuracy |
+|------|---------------|-----------------|-------------|----------|----------------|--------------|
+| 100  | 0.632200      | 0.811809        | 84.749290   | 84.749290| 0.847493       | 0.899243     |
+| 200  | 0.751500      | 0.627198        | 84.768212   | 84.768212| 0.847682       | 0.899243     |
+| 300  | 0.662600      | 0.557515        | 86.244087   | 86.244087| 0.862441       | 0.899243     |
+| 400  | 0.600400      | 0.567693        | 86.177862   | 86.177862| 0.861779       | 0.899243     |
+| 500  | 0.613200      | 0.523546        | 86.499527   | 86.499527| 0.864995       | 0.899243     |
+| 600  | 0.495200      | 0.539225        | 86.565752   | 86.565752| 0.865658       | 0.899243     |
+| 700  | 0.645300      | 0.552358        | 85.354778   | 85.354778| 0.853548       | 0.899243     |
+| 800  | 0.499100      | 0.562317        | 86.338694   | 86.338694| 0.863387       | 0.899243     |
+| 900  | 0.482800      | 0.499747        | 86.811731   | 86.811731| 0.868117       | 0.899243     |
+| 1000 | 0.372800      | 0.543513        | 86.972564   | 86.972564| 0.869726       | 0.900000     |
+| 1100 | 0.554000      | 0.502747        | 85.969726   | 85.969726| 0.859697       | 0.894797     |
+| 1200 | 0.459800      | 0.484941        | 87.019868   | 87.019868| 0.870199       | 0.900662     |
+| 1300 | 0.463600      | 0.477527        | 87.407758   | 87.407758| 0.874078       | 0.899905     |
+| 1400 | 0.356800      | 0.499119        | 87.549669   | 87.549669| 0.875497       | 0.901608     |
+| 1500 | 0.494200      | 0.485287        | 87.549669   | 87.549669| 0.875497       | 0.901703     |
+| 1600 | 0.521100      | 0.466062        | 87.284768   | 87.284768| 0.872848       | 0.899243     |
+| 1700 | 0.461200      | 0.462704        | 87.540208   | 87.540208| 0.875402       | 0.901419     |
+| 1800 | 0.415700      | 0.474295        | 87.691580   | 87.691580| 0.876916       | 0.901892     |
+| 1900 | 0.622900      | 0.462900        | 87.417219   | 87.417219| 0.874172       | 0.901987     |
+| 2000 | 0.477800      | 0.465936        | 87.568590   | 87.568590| 0.875686       | 0.901892     |
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
 
-[More Information Needed]
 
 
-#### Training Hyperparameters
+### Training Data  
+The model was trained on the [SQuAD](https://huggingface.co/datasets/squad) dataset, a widely used benchmark for context-based question-answering tasks. It consists of passages from Wikipedia and corresponding questions, with human-annotated answers.  
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+During training, the dataset was processed to extract contexts, questions, and answers, ensuring compatibility with the BERT architecture for QA. The training utilized free resources to minimize costs and focus on model efficiency.  
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+### Training Procedure  
 
-## Evaluation
+**Training Objective**  
+The model was trained with the objective of performing context-based question answering using the SQuAD dataset. The fine-tuning process adapts BERT's masked language model (MLM) architecture for QA tasks by leveraging its ability to encode contextual relationships between the passage, question, and answer.  
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+**Optimization**  
+The training utilized the AdamW optimizer with a linear learning rate scheduler and warm-up steps to ensure effective weight updates and prevent overfitting. The training was run for 2000 steps, with early stopping applied based on the validation loss and exact match score.
 
-### Testing Data, Factors & Metrics
+**Hardware and Resources**  
+Training was conducted on free resources, such as Google Colab or equivalent free GPU resources. While this limited the scale, adjustments in batch size and learning rate were optimized to make the training efficient within these constraints.   
 
-#### Testing Data
+**Unique Features**  
+The model fine-tuning procedure emphasizes efficient learning, leveraging BERT's pre-trained knowledge while adapting it specifically to QA tasks in a resource-constrained environment.  
 
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
 
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+#### Metrics
 
-[More Information Needed]
+Performance was evaluated using the following metrics:  
+- **Exact Match (EM)**: Measures the percentage of predictions that match the ground-truth answers exactly.  
+- **F1 Score**: Assesses the overlap between the predicted and true answers at a token level, balancing precision and recall.  
+- **Start and End Accuracy**: Tracks the model’s ability to correctly identify the start and end indices of answers within the context. 
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
+The model trained on the SQuAD dataset achieved the following key performance metrics:
 
-## Model Examination [optional]
+Exact Match (EM): Up to 87.69%
+F1 Score: Up to 87.69%
+Validation Loss: Reduced to 0.46
+Start Accuracy: Peaked at 87.69%
+End Accuracy: Peaked at 90.19%
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
+#### Summary
+#### Summary
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+The model, **bert_squad**, was fine-tuned for context-based question answering using the SQuAD dataset from Hugging Face. Key metrics include an Exact Match (EM) and F1 score of up to **87.69%**, demonstrating strong accuracy. Performance benchmarks show consistent improvement in loss and accuracy over 2000 steps, with validation loss reaching as low as **0.46**. 
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+The training utilized free resources, leveraging BERT’s robust pretraining, although BERT’s limitation as a Masked Language Model (MLM) remains a consideration. This work highlights the potential for effective question-answering systems built on pre-existing datasets and infrastructure.
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses BERT, a pre-trained Transformer-based architecture, fine-tuned for context-based question answering tasks. It aims to predict answers based on the given input text and context.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+GPU: Tesla P100, NVIDIA T4
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+Framework: Hugging Face Transformers
+Dataset: SQuAD (from Hugging Face)
+Other tools: Python, PyTorch
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
+@misc{bert_squad_finetune,
+  title = {BERT Fine-tuned for SQuAD},
+  author = {Your Name or Team Name},
+  year = {2024},
+  url = {https://huggingface.co/your-model-repository}
+}
 
-**APA:**
 
-[More Information Needed]
 
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Exact Match (EM): A metric measuring the percentage of predictions that match the ground truth exactly.
+F1 Score: The harmonic mean of precision and recall, used for evaluating the quality of the predictions.
+Masked Language Model (MLM): Pre-training objective for BERT, predicting masked words in input sentences.