README.md

metadata

license: mit
datasets:
  - squad_v2
  - squad
language:
  - en
library_name: transformers
pipeline_tag: question-answering
tags:
  - deberta
  - deberta-v3
  - mdeberta
  - question-answering
  - squad
  - squad_v2
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

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

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

# 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
}