
# mdeberta-v3-base for Extractive QA

This is the [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-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:** mdeberta-v3-base  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** SQuAD 2.0  
**Eval data:** SQuAD 2.0  
**Infrastructure**: 1x NVIDIA T4  

### Model Usage
```python
import torch
from transformers import(
  AutoModelForQuestionAnswering,
  AutoTokenizer,
  pipeline
)
model_name = "sjrhuschlee/mdeberta-v3-base-squad2"

# 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": 77.7327935222672,
    "eval_HasAns_f1": 84.7172608568916,
    "eval_HasAns_total": 5928,
    "eval_NoAns_exact": 83.02775441547519,
    "eval_NoAns_f1": 83.02775441547519,
    "eval_NoAns_total": 5945,
    "eval_best_exact": 80.38406468457846,
    "eval_best_exact_thresh": 0.0,
    "eval_best_f1": 83.87129810154576,
    "eval_best_f1_thresh": 0.0,
    "eval_exact": 80.38406468457846,
    "eval_f1": 83.87129810154582,
    "eval_runtime": 221.4855,
    "eval_samples": 12054,
    "eval_samples_per_second": 54.423,
    "eval_steps_per_second": 2.271,
    "eval_total": 11873
}

# Squad
{
    "eval_exact_match": 83.78429517502366,
    "eval_f1": 90.96354972948996,
    "eval_runtime": 197.0364,
    "eval_samples": 10715,
    "eval_samples_per_second": 54.381,
    "eval_steps_per_second": 2.269
}
```
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["deberta", "deberta-v3", "mdeberta", "question-answering", "squad", "squad_v2"], "datasets": ["squad_v2", "squad"], "pipeline_tag": "question-answering", "model-index": [{"name": "sjrhuschlee/mdeberta-v3-base-squad2", "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": 80.3841, "name": "Exact Match"}, {"type": "f1", "value": 83.8713, "name": "F1"}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 83.7843, "name": "Exact Match"}, {"type": "f1", "value": 90.9635, "name": "F1"}]}]}]}
transformers pytorch safetensors deberta-v2 question-answering deberta deberta-v3 mdeberta squad squad_v2 en dataset:squad_v2 dataset:squad license:mit model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #mdeberta #squad #squad_v2 #en #dataset-squad_v2 #dataset-squad #license-mit #model-index #endpoints_compatible #region-us 


# mdeberta-v3-base for Extractive QA

This is the mdeberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.

## Overview
Language model: mdeberta-v3-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA T4  

### Model Usage


### Metrics

# mdeberta-v3-base for Extractive QA

This is the mdeberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. ## Overview
Language model: mdeberta-v3-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA T4 ### Model Usage ### Metrics
TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #deberta #deberta-v3 #mdeberta #squad #squad_v2 #en #dataset-squad_v2 #dataset-squad #license-mit #model-index #endpoints_compatible #region-us 
 # mdeberta-v3-base for Extractive QA

This is the mdeberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. ## Overview
Language model: mdeberta-v3-base  
Language: English  
Downstream-task: Extractive QA  
Training data: SQuAD 2.0  
Eval data: SQuAD 2.0  
Infrastructure: 1x NVIDIA T4 ### Model Usage ### Metrics