flan t5 base squad2 EU AI Act Compliance Profile

flan-t5-base for Extractive QA

This is the flan-t5-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.

UPDATE: With transformers version 4.31.0 the use_remote_code=True is no longer necessary.

NOTE: The <cls> token must be manually added to the beginning of the question for this model to work properly. It uses the <cls> token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually.

Overview

Language model: flan-t5-base
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x NVIDIA 3070

Model Usage

import torch
from transformers import(
  AutoModelForQuestionAnswering,
  AutoTokenizer,
  pipeline
)
model_name = "sjrhuschlee/flan-t5-base-squad2"

# a) Using pipelines
nlp = pipeline(
  'question-answering',
  model=model_name,
  tokenizer=model_name,
  # trust_remote_code=True, # Do not use if version transformers>=4.31.0
)
qa_input = {
'question': f'{nlp.tokenizer.cls_token}Where do I live?',  # '<cls>Where do I live?'
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# {'score': 0.980, 'start': 30, 'end': 37, 'answer': ' London'}

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(
  model_name,
  # trust_remote_code=True # Do not use if version transformers>=4.31.0
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

question = f'{tokenizer.cls_token}Where do I live?'  # '<cls>Where do I live?'
context = 'My name is Sarah and I live in London'
encoding = tokenizer(question, context, return_tensors="pt")
output = model(
  encoding["input_ids"],
  attention_mask=encoding["attention_mask"]

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Library

Base Model