File size: 3,999 Bytes
cb2b286 f451ebc b315c71 cb2b286 f451ebc f3eda55 5dfabeb f3eda55 f451ebc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
license: mit
datasets:
- squad_v2
- squad
language:
- en
library_name: transformers
tags:
- question-answering
- squad
- squad_v2
- t5
- lora
- peft
---
# 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.
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)
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)
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")
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": 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"
``` |