Update README.md

README.md

@@ -1,3 +1,245 @@
 ---
 license: mit
+datasets:
+- squad_v2
+- squad
+language:
+- en
+library_name: transformers
+pipeline_tag: question-answering
+tags:
+- deberta
+- deberta-v3
+- question-answering
+- squad
+- squad_v2
+model-index:
+- name: sjrhuschlee/deberta-v3-base-squad2-ext-v1
+  results:
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_v2
+      type: squad_v2
+      config: squad_v2
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 79.483
+      name: Exact Match
+    - type: f1
+      value: 82.343
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad
+      type: squad
+      config: plain_text
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 85.894
+      name: Exact Match
+    - type: f1
+      value: 91.298
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: adversarial_qa
+      type: adversarial_qa
+      config: adversarialQA
+      split: validation
+    metrics:
+      - type: exact_match
+        value: 44.867
+        name: Exact Match
+      - type: f1
+        value: 55.996
+        name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_adversarial
+      type: squad_adversarial
+      config: AddOneSent
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 80.190
+      name: Exact Match
+    - type: f1
+      value: 85.028
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts
+      type: squadshifts
+      config: amazon
+      split: test
+    metrics:
+      - type: exact_match
+        value: 69.712
+        name: Exact Match
+      - type: f1
+        value: 81.171
+        name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts
+      type: squadshifts
+      config: new_wiki
+      split: test
+    metrics:
+      - type: exact_match
+        value: 81.544
+        name: Exact Match
+      - type: f1
+        value: 89.782
+        name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts
+      type: squadshifts
+      config: nyt
+      split: test
+    metrics:
+      - type: exact_match
+        value: 80.05
+        name: Exact Match
+      - type: f1
+        value: 87.756
+        name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts
+      type: squadshifts
+      config: reddit
+      split: test
+    metrics:
+      - type: exact_match
+        value: 60.481
+        name: Exact Match
+      - type: f1
+        value: 68.686
+        name: F1
 ---
+
+# deberta-v3-base for Extractive QA
+
+This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.
+
+## Overview
+**Language model:** deberta-v3-base  
+**Language:** English  
+**Downstream-task:** Extractive QA  
+**Training data:** SQuAD 2.0  
+**Eval data:** SQuAD 2.0  
+**Infrastructure**: 1x NVIDIA 3070  
+
+## Model Usage
+```python
+import torch
+from transformers import(
+  AutoModelForQuestionAnswering,
+  AutoTokenizer,
+  pipeline
+)
+model_name = "sjrhuschlee/deberta-v3-base-squad2-ext-v1"
+
+# a) Using pipelines
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+qa_input = {
+'question': 'Where do I live?',
+'context': 'My name is Sarah and I live in London'
+}
+res = nlp(qa_input)
+# {'score': 0.984, 'start': 30, 'end': 37, 'answer': ' London'}
+
+# b) Load model & tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+question = 'Where do I live?'
+context = 'My name is Sarah and I live in London'
+encoding = tokenizer(question, context, return_tensors="pt")
+start_scores, end_scores = model(
+  encoding["input_ids"],
+  attention_mask=encoding["attention_mask"],
+  return_dict=False
+)
+
+all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
+answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
+answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
+# 'London'
+```
+
+## Metrics
+
+```bash
+# Squad v2
+{
+    "eval_HasAns_exact": 84.36234817813765,
+    "eval_HasAns_f1": 90.09079905537246,
+    "eval_HasAns_total": 5928,
+    "eval_NoAns_exact": 74.61732548359966,
+    "eval_NoAns_f1": 74.61732548359966,
+    "eval_NoAns_total": 5945,
+    "eval_best_exact": 79.45759285774446,
+    "eval_best_exact_thresh": 0.0,
+    "eval_best_f1": 82.31771724081922,
+    "eval_best_f1_thresh": 0.0,
+    "eval_exact": 79.48286027120358,
+    "eval_f1": 82.34298465427844,
+    "eval_runtime": 109.7262,
+    "eval_samples": 11951,
+    "eval_samples_per_second": 108.917,
+    "eval_steps_per_second": 4.539,
+    "eval_total": 11873
+}
+
+# Squad
+{
+    "eval_exact": 85.89403973509934,
+    "eval_f1": 91.2982923196374,
+    "eval_runtime": 96.6499,
+    "eval_samples": 10618,
+    "eval_samples_per_second": 109.86,
+    "eval_steps_per_second": 4.584,
+    "eval_total": 10570
+}
+```
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-06
+- train_batch_size: 12
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 96
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3.0
+
+### Framework versions
+
+- Transformers 4.31.0.dev0