|
--- |
|
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: 86.785 |
|
name: Exact Match |
|
- type: f1 |
|
value: 89.537 |
|
name: F1 |
|
- task: |
|
type: question-answering |
|
name: Question Answering |
|
dataset: |
|
name: squad |
|
type: squad |
|
config: plain_text |
|
split: validation |
|
metrics: |
|
- type: exact_match |
|
value: 85.998 |
|
name: Exact Match |
|
- type: f1 |
|
value: 91.296 |
|
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: 35.767 |
|
name: Exact Match |
|
- type: f1 |
|
value: 45.565 |
|
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: 75.322 |
|
name: Exact Match |
|
- type: f1 |
|
value: 79.327 |
|
name: F1 |
|
- task: |
|
type: question-answering |
|
name: Question Answering |
|
dataset: |
|
name: squadshifts amazon |
|
type: squadshifts |
|
config: amazon |
|
split: test |
|
metrics: |
|
- type: exact_match |
|
value: 74.153 |
|
name: Exact Match |
|
- type: f1 |
|
value: 86.567 |
|
name: F1 |
|
- task: |
|
type: question-answering |
|
name: Question Answering |
|
dataset: |
|
name: squadshifts |
|
type: squadshifts |
|
config: new_wiki |
|
split: test |
|
metrics: |
|
- type: exact_match |
|
value: 81.053 |
|
name: Exact Match |
|
- type: f1 |
|
value: 89.043 |
|
name: F1 |
|
- task: |
|
type: question-answering |
|
name: Question Answering |
|
dataset: |
|
name: squadshifts |
|
type: squadshifts |
|
config: nyt |
|
split: test |
|
metrics: |
|
- type: exact_match |
|
value: 83.815 |
|
name: Exact Match |
|
- type: f1 |
|
value: 90.416 |
|
name: F1 |
|
- task: |
|
type: question-answering |
|
name: Question Answering |
|
dataset: |
|
name: squadshifts |
|
type: squadshifts |
|
config: reddit |
|
split: test |
|
metrics: |
|
- type: exact_match |
|
value: 73.212 |
|
name: Exact Match |
|
- type: f1 |
|
value: 83.214 |
|
name: F1 |
|
--- |
|
|
|
# flan-t5-large for Extractive QA |
|
|
|
This is the [flan-t5-large](https://huggingface.co/google/flan-t5-large) 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. |
|
|
|
**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](https://github.com/huggingface/peft). |
|
|
|
**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. |
|
```python |
|
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 |
|
|
|
```bash |
|
# 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. |
|
```python |
|
#!pip install peft |
|
|
|
from peft import LoraConfig, PeftModelForQuestionAnswering |
|
from transformers import AutoModelForQuestionAnswering, AutoTokenizer |
|
model_name = "sjrhuschlee/flan-t5-large-squad2" |
|
``` |
