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T5 base finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese

Exemple of what can do with a T5 model (for example: Question Answering finetuned on SQUAD v1.1 in Portuguese)

Introduction

t5-base-qa-squad-v1.1-portuguese is a QA model (Question Answering) in Portuguese that was finetuned on 27/01/2022 in Google Colab from the model unicamp-dl/ptt5-base-portuguese-vocab of Neuralmind on the dataset SQUAD v1.1 in portuguese from the Deep Learning Brasil group by using a Test2Text-Generation objective.

Due to the small size of T5 base and finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset:

  • f1: 79.3
  • exact_match: 67.3983

Check our other QA models in Portuguese finetuned on SQUAD v1.1:

Blog post

NLP nas empresas | Como eu treinei um modelo T5 em português na tarefa QA no Google Colab (27/01/2022)

Widget & App

You can test this model into the widget of this page.

Use as well the QA App | T5 base pt that allows using the model T5 base finetuned on the QA task with the SQuAD v1.1 pt dataset.

Using the model for inference in production

# install pytorch: check https://pytorch.org/
# !pip install transformers 
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# model & tokenizer
model_name = "pierreguillou/t5-base-qa-squad-v1.1-portuguese"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# parameters
max_target_length=32
num_beams=1
early_stopping=True

input_text  = 'question: Quando foi descoberta a Covid-19? context: A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.'
label = '1 de dezembro de 2019'

inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(inputs["input_ids"],
                             max_length=max_target_length, 
                             num_beams=num_beams, 
                             early_stopping=early_stopping
                            )
pred = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
             
print('true answer |', label)
print('pred        |', pred)

You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence.

!pip install transformers
import transformers
from transformers import pipeline

# model
model_name = "pierreguillou/t5-base-qa-squad-v1.1-portuguese"

# parameters
max_target_length=32
num_beams=1
early_stopping=True
clean_up_tokenization_spaces=True

input_text  = 'question: Quando foi descoberta a Covid-19? context: A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.'
label = '1 de dezembro de 2019'
    
text2text = pipeline(
                    "text2text-generation",
                     model=model_name,
                     max_length=max_target_length, 
                     num_beams=num_beams, 
                     early_stopping=early_stopping,
                     clean_up_tokenization_spaces=clean_up_tokenization_spaces
                    )

pred = text2text(input_text)

print('true answer |', label)
print('pred        |', pred)

Training procedure

Notebook

The notebook of finetuning (HuggingFace_Notebook_t5-base-portuguese-vocab_question_answering_QA_squad_v11_pt.ipynb) is in github.

Hyperparameters

# do training and evaluation
do_train = True
do_eval= True

# batch
batch_size = 4
gradient_accumulation_steps = 3
per_device_train_batch_size = batch_size
per_device_eval_batch_size = per_device_train_batch_size*16

# LR, wd, epochs
learning_rate = 1e-4
weight_decay = 0.01
num_train_epochs = 10
fp16 = True

# logs
logging_strategy = "steps"
logging_first_step = True 
logging_steps = 3000     # if logging_strategy = "steps"
eval_steps = logging_steps 

# checkpoints
evaluation_strategy = logging_strategy
save_strategy = logging_strategy
save_steps = logging_steps
save_total_limit = 3

# best model
load_best_model_at_end = True
metric_for_best_model = "f1" #"loss"
if metric_for_best_model == "loss":
  greater_is_better = False
else:
  greater_is_better = True  

# evaluation
num_beams = 1

Training results

Num examples = 87510
Num Epochs = 10
Instantaneous batch size per device = 4
Total train batch size (w. parallel, distributed & accumulation) = 12
Gradient Accumulation steps = 3
Total optimization steps = 72920
 
Step 	Training Loss  Exact Match	F1
3000 	0.776100	     61.807001  	75.114517
6000 	0.545900	     65.260170  	77.468930
9000 	0.460500	     66.556291  	78.491938
12000	0.393400	     66.821192  	78.745397
15000	0.379800  	   66.603595  	78.815515
18000	0.298100  	   67.578051  	79.287899
21000	0.303100  	   66.991485  	78.979669
24000	0.251600  	   67.275307  	78.929923

27000	0.237500	     66.972564  	79.333612

30000	0.220500 	    66.915799  	79.236574
33000	0.182600	     67.029328  	78.964212
36000	0.190600 	    66.982025  	79.086125
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Datasets used to train pierreguillou/t5-base-qa-squad-v1.1-portuguese

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Evaluation results