metadata
language:
- en
license: mit
library_name: transformers
tags:
- question-answering
- squad
- squad_v2
- t5
- lora
- peft
datasets:
- squad_v2
- squad
base_model: google/flan-t5-large
model-index:
- name: sjrhuschlee/flan-t5-large-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: 86.819
name: Exact Match
- type: f1
value: 89.569
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: exact_match
value: 89.357
name: Exact Match
- type: f1
value: 95.06
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: 48.833
name: Exact Match
- type: f1
value: 62.555
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: 84.835
name: Exact Match
- type: f1
value: 90.245
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts amazon
type: squadshifts
config: amazon
split: test
metrics:
- type: exact_match
value: 76.722
name: Exact Match
- type: f1
value: 89.68
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts new_wiki
type: squadshifts
config: new_wiki
split: test
metrics:
- type: exact_match
value: 84.316
name: Exact Match
- type: f1
value: 92.967
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts nyt
type: squadshifts
config: nyt
split: test
metrics:
- type: exact_match
value: 86.925
name: Exact Match
- type: f1
value: 94.064
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts reddit
type: squadshifts
config: reddit
split: test
metrics:
- type: exact_match
value: 78.241
name: Exact Match
- type: f1
value: 89.243
name: F1
flan-t5-large for Extractive QA
This is the flan-t5-large 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.
This model was trained using LoRA available through the PEFT library.
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-large
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Infrastructure: 1x NVIDIA 3070
Model Usage
Using Transformers
This uses the merged weights (base model weights + LoRA weights) to allow for simple use in Transformers pipelines. It has the same performance as using the weights separately when using the PEFT library.
import torch
from transformers import(
AutoModelForQuestionAnswering,
AutoTokenizer,
pipeline
)
model_name = "sjrhuschlee/flan-t5-large-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': 0.984, '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"]
)
all_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist())
answer_tokens = all_tokens[torch.argmax(output["start_logits"]):torch.argmax(output["end_logits"]) + 1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# 'London'
Metrics
# Squad v2
{
"eval_HasAns_exact": 85.08771929824562,
"eval_HasAns_f1": 90.598422845031,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 88.47771236333053,
"eval_NoAns_f1": 88.47771236333053,
"eval_NoAns_total": 5945,
"eval_best_exact": 86.78514276088605,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 89.53654936623764,
"eval_best_f1_thresh": 0.0,
"eval_exact": 86.78514276088605,
"eval_f1": 89.53654936623776,
"eval_runtime": 1908.3189,
"eval_samples": 12001,
"eval_samples_per_second": 6.289,
"eval_steps_per_second": 0.787,
"eval_total": 11873
}
# Squad
{
"eval_HasAns_exact": 85.99810785241249,
"eval_HasAns_f1": 91.296119057944,
"eval_HasAns_total": 10570,
"eval_best_exact": 85.99810785241249,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 91.296119057944,
"eval_best_f1_thresh": 0.0,
"eval_exact": 85.99810785241249,
"eval_f1": 91.296119057944,
"eval_runtime": 1508.9596,
"eval_samples": 10657,
"eval_samples_per_second": 7.062,
"eval_steps_per_second": 0.883,
"eval_total": 10570
}
Using with Peft
NOTE: This requires code in the PR https://github.com/huggingface/peft/pull/473 for the PEFT library.
#!pip install peft
from peft import LoraConfig, PeftModelForQuestionAnswering
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model_name = "sjrhuschlee/flan-t5-large-squad2"