giotvr/bertimbau_large_assin2_fine_tuned

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This is a BERTimbau-base fine-tuned model on 5K (premise, hypothesis) sentence pairs from the ASSIN2 (Avaliação de Similaridade Semântica e Inferência Textual) corpus. The original references are: Unsupervised Cross-Lingual Representation Learning At Scale, ASSIN2: Avaliação de Similaridade Semântica e Inferência Textual, respectivelly. This model is suitable for Brazilian Portuguese.

Model Details

Model Description

• Developed by: Giovani Tavares and Felipe Ribas Serras
• Oriented By: Felipe Ribas Serras, Renata Wassermann and Marcelo Finger
• Model type: Transformer-based text classifier
• Language(s) (NLP): Portuguese
• License: mit
• Finetuned from model BERTimbau-base

Model Sources

• Repository: Natural-Portuguese-Language-Inference
• Paper: This is an ongoing research. We are currently writing a paper where we fully describe our experiments.

Uses

Direct Use

This fine-tuned version of BERTimbau-base performs Natural Language Inference (NLI), which is a text classification task.

The (premise, hypothesis) entailment definition used is the same as the one found in Salvatore's paper [1].

Therefore, this fine-tuned version of BERTimbau-base classifies pairs of sentences in the form (premise, hypothesis) into the classes ENTAILMENT or NONE.

Demo

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_path = "giotvr/bertimbau_large_assin2_fine_tuned"
premise = "As mudanças climáticas são uma ameaça séria para a biodiversidade do planeta."
hypothesis ="A biodiversidade do planeta é seriamente ameaçada pelas mudanças climáticas."
tokenizer = XLMRobertaTokenizer.from_pretrained(model_path, use_auth_token=True)
input_pair = tokenizer(premise, hypothesis, return_tensors="pt",padding=True, truncation=True)
model = AutoModelForSequenceClassification.from_pretrained(model_path, use_auth_token=True)

with torch.no_grad():
    logits = model(**input_pair).logits
probs = torch.nn.functional.softmax(logits, dim=-1)
probs, sorted_indices = torch.sort(probs, descending=True)
for i, score in enumerate(probs[0]):
    print(f"Class {sorted_indices[0][i]}: {score.item():.4f}")

Recommendations

This model should be used for scientific purposes only. It was not tested for production environments.

Fine-Tuning Details

Fine-Tuning Data

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• Train Dataset: ASSIN2
  

• Evaluation Dataset used for Hyperparameter Tuning: PLUE/MNLI's validation split

• Test Datasets:

  • ASSIN's test split
  • ASSIN2's test split
  • PLUE/MNLI's validation matched split

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This is a fine tuned version of BERTimbau-base using the ASSIN2 (Avaliação de Similaridade Semântica e Inferência textual) dataset. ASSIN2 is a corpus annotated with hypothesis/premise Portuguese sentence pairs suitable for detecting textual entailment or neutral relationship between the members of such pairs. Such corpus is balanced with 7k ptbr (Brazilian Portuguese) sentence pairs.

Fine-Tuning Procedure

The model's fine-tuning procedure can be summarized in three major subsequent tasks:

1. Data Processing:
ASSIN2's validation and train splits were loaded from the Hugging Face Hub and processed afterwards;
2. Hyperparameter Tuning:
BERTimbau-base's hyperparameters were chosen with the help of the [Weights & Biases] API to track the results and upload the fine-tuned models;
3. Final Model Loading and Testing:
using the cross-tests approach described in the this section, the models' performance were measured using different datasets and metrics.

Hyperparameter Tuning

The following hyperparameters were tested in order to maximize the evaluation accuracy.

• Number of Training Epochs: $(2,3,4)$
• Per Device Train Batch Size: $(8,16,32)$
• Learning Rate: $(3e−5, 2e−5, 3e−5)$

The hyperparemeter tuning experiments were run and tracked using the Weights & Biases' API and can be found at this link.

Training Hyperparameters

The hyperparameter tuning performed yelded the following values:

• Number of Training Epochs: $3$
• Per Device Train Batch Size: $32$
• Learning Rate: $5e-5$

Evaluation

ASSIN

Testing this model in ASSIN's test split was straightforward because this model was fine tuned using ASSIN2's training set which contains the same labels as ASSIN. Hence, it can predict the same labels as the ones found in ASSIN's test set.

ASSIN2

Testing this model in ASSIN2's test split is straightforward because this model was fine tuned using ASSIN2's training set and therefore can predict the same labels as the ones found in its test set.

PLUE/MNLI

Testing this model in PLUE/MNLI was only possible by considering PLUE/MNLI's contradiction and neutral labels as NONE and PLUE/MNLI's entailment label as equivalent to the ENTAILMENT predicted by the model.

More information on how such mapping is performed can be found in Modelos para Inferência em Linguagem Natural que entendem a Língua Portuguesa.

Metrics

The model's performance metrics for each test dataset are presented separately. Accuracy, f1 score, precision and recall were the metrics used to every evaluation performed. Such metrics are reported below. More information on such metrics them will be available in our ongoing research paper.

Results

test set	accuracy	f1 score	precision	recall
assin	0.78	0.78	0.80	0.78
assin2	0.89	0.89	0.90	0.90
plue/mnli	0.68	0.58	0.74	0.68

Model Examination

Some interpretability work is being done in order to understand the model's behavior. Such details will be available in the previoulsy referred paper.

References

[1]Salvatore, F. S. (2020). Analyzing Natural Language Inference from a Rigorous Point of View (pp. 1-2).