Abdo36
/

Bert-SquAD-QA

@@ -1,199 +1,76 @@
----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[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 -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[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]
-## Training Details
-### 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- 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 regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- 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. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **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]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- 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]
-**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]

+---
+language:
+  - en
+tags:
+  - question-answering
+  - transformers
+  - bert
+  - squad
+license: apache-2.0
+datasets:
+  - squad
+model-name: bert-base-uncased-finetuned-squad
+library_name: transformers
+---
+# BERT-Base Uncased Fine-Tuned on SQuAD
+## Overview
+This repository contains a **BERT-Base Uncased** model fine-tuned on the **SQuAD (Stanford Question Answering Dataset)** for **Question Answering (QA) tasks**. The model has been fine-tuned for **2 epochs**, making it suitable for extracting answers from given contexts by predicting start and end token positions.
+## The Model predicts 2 probabilities among all the tokens in the vocab , One indicating the start token and the other indicating the end token, Then the answer between both these tokens are extracted.
+## Model Details
+- **Model Type**: BERT-Base Uncased
+- **Fine-Tuning Dataset**: SQuAD (Stanford Question Answering Dataset)
+- **Number of Epochs**: 2
+- **Task**: Question Answering
+- **Base Model**: [BERT-Base Uncased](https://huggingface.co/bert-base-uncased)
+---
+## Usage
+### How to Load the Model
+You can load the model using the `transformers` library from Hugging Face:
+```python
+from transformers import BertForQuestionAnswering, BertTokenizer
+# Load the tokenizer and model
+tokenizer = BertTokenizer.from_pretrained("Abdo36/Bert-SquAD-QA")
+model = BertForQuestionAnswering.from_pretrained("Abdo36/Bert-SquAD-QA")
+context = "BERT is a method of pre-training language representations."
+question = "What is BERT?"
+inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
+# Perform inference
+outputs = model(**inputs)
+start_scores = outputs.start_logits
+end_scores = outputs.end_logits
+# Extract answer
+start_index = start_scores.argmax()
+end_index = end_scores.argmax()
+answer = tokenizer.decode(inputs["input_ids"][0][start_index:end_index + 1])
+print("Answer:", answer)
+```
+## Citation
+If you use this model in your research, please cite the original BERT paper:
+```bibtex
+@article{devlin2018bert,
+  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
+  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
+  journal={arXiv preprint arXiv:1810.04805},
+  year={2018}
+}
+```