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README.md

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+---
+datasets:
+- squad_v2
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: question-answering
+tags:
+- question-answering
+---
+# QA-BERT
+QA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there.
+
+## Dataset
+The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike.
+Due to GPU limitations, this version is trained on `30k samples` from the Stanford Question Answering Dataset.
+
+<details>
+ <summary><i>Structure of the Data Dictonary</i></summary>
+<!--All you need is a blank line-->
+
+      {
+      "data":[
+          {
+              "title":"Article Title",
+              "paragraphs":[
+                  {
+                      "context":"The context text of the paragraph",
+                      "qas":[
+                          {
+                              "question":"The question asked about the context",
+                              "id":"A unique identifier for the question",
+                              "answers":[
+                                  {
+                                      "text":"The answer to the question",
+                                      "answer_start":"The starting index of the answer in the context"
+                                  }
+                              ]
+                          }
+                      ]
+                  }
+              ]
+          }
+      ],
+      "version":"The version of the SQuAD dataset"
+      }
+</details>
+
+## Model
+BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language processing tasks such as question answering. BERT is fine-tuned for question answering by adding a linear layer on top of the pre-trained BERT representations to predict the start and end of the answer in the input context. BERT has achieved state-of-the-art results on multiple benchmark datasets, including the Stanford Question Answering Dataset (SQuAD). The fine-tuning process allows BERT to effectively capture the relationships between questions and answers and generate accurate answers.
+<img src="https://imgs.search.brave.com/F8m-nwp6EIG5vq--OmJLrCDpIkuX6tEQ_kyFKQjlUTs/rs:fit:1200:1200:1/g:ce/aHR0cHM6Ly9ibG9n/LmdyaWRkeW5hbWlj/cy5jb20vY29udGVu/dC9pbWFnZXMvMjAy/MC8xMC9TbGljZS0x/OC5wbmc">
+For more detail about this read [Understanding QABERT](https://github.com/SRDdev/AnswerMind)
+
+
+## Inference
+_Load model_
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+QAtokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small")
+
+QAmodel = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small")
+```
+
+_context_
+```text
+Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
+question-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
+a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
+```
+
+
+_Build Pipeline_
+```python
+from transformers import pipeline
+
+ask = pipeline("question-answering", model= QAmodel , tokenizer = QAtokenizer)
+
+result = ask(question="What is a good example of a question answering dataset?", context=context)
+
+print(f"Answer: '{result['answer']}'")
+```
+
+## Contributing
+
+Pull requests are welcome. For major changes, please open an issue first
+to discuss what you would like to change.
+Please make sure to update tests as appropriate.
+
+## Citations
+```
+@citation{ QA-BERT-small,
+  author = {Shreyas Dixit},
+  year = {2023},
+  url = {https://huggingface.co/SRDdev/QA-BERT-small}
+}
+```

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForQuestionAnswering"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "vocab_size": 30522
+}

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special_tokens_map.json

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tokenizer_config.json

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