xlm_roberta_base_assin2_fine_tuned

Model Card for Model ID

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This is a XLM-RoBERTa-base fine-tuned model on 6.5K (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

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• 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 XLM-RoBERTa-base

Model Sources

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

Uses

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Direct Use

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

<!-- <div id="assin_function"> **Definition 1.** Given a pair of sentences $$(premise, hypothesis)$, let $\hat{f}^{(xlmr\_base)}$ be the fine-tuned models' inference function: $$ \hat{f}^{(xlmr\_base)} = \begin{cases} ENTAILMENT, & \text{if $premise$ entails $hypothesis$}\\ PARAPHRASE, & \text{if $premise$ entails $hypothesis$ and $hypothesis$ entails $premise$}\\ NONE & \text{otherwise} \end{cases} $$ </div> -->

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

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

<!-- ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. -->

Demo

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_path = "giotvr/xlm_roberta_base_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

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

<!-- ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] -->

Fine-Tuning Details

Fine-Tuning Data

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---

• Train Dataset: ASSIN <br>

• Evaluation Dataset used for Hyperparameter Tuning: ASSIN2's validation split

• Test Datasets:

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

---

This is a fine tuned version of XLM-RoBERTa-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

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The model's fine-tuning procedure can be summarized in three major subsequent tasks: <ol type="i"> <li>Data Processing:</li> ASSIN2's validation and train splits were loaded from the Hugging Face Hub and processed afterwards; <li>Hyperparameter Tuning:</li> XLM-RoBERTa-base's hyperparameters were chosen with the help of the [Weights & Biases] API to track the results and upload the fine-tuned models; <li>Final Model Loading and Testing:</li> using the cross-tests approach described in the this section, the models' performance were measured using different datasets and metrics. </ol>

<!-- ##### Column Renaming The **Hugging Face**'s ```transformers``` module's ```DataCollator``` used by its ```Trainer``` requires that the ```class label``` column of the collated dataset to be called ```label```. [ASSIN](https://huggingface.co/datasets/assin)'s class label column for each hypothesis/premise pair is called ```entailment_judgement```. Therefore, as the first step of the data preprocessing pipeline the column ```entailment_judgement``` was renamed to ```label``` so that the **Hugging Face**'s ```transformers``` module's ```Trainer``` could be used. -->

Hyperparameter Tuning

<!-- The model's training hyperparameters were chosen according to the following definition: <div id="hyperparameter_tuning"> **Definition 2.** Let $Hyperparms= \{i: i \text{ is an hyperparameter of } \hat{f}^{(xlmr\_base)}\}$ and $\hat{f}^{(xlmr\_base)}$ be the model's inference function defined in [Definition 1](#assin_function) : $$ Hyperparms = \argmax_{hyp}(eval\_acc(\hat{f}^{(xlmr\_base)}_{hyp}, assin\_validation)) $$ </div> -->

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

• Number of Training Epochs: $(1,2,3)$
• Per Device Train Batch Size: $(8,16,32)$
• Learning Rate: $(1e−5, 2e−5, 5e−5)$

The hyperaparemeter 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: $8$
• 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

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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

Model Examination

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

<!--## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] --> <!-- ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. **BibTeX:** ```bibtex @article{tcc_paper, author = {Giovani Tavares and Felipe Ribas Serras and Renata Wassermann and Marcelo Finger}, title = {Modelos Transformer para Inferência de Linguagem Natural em Português}, pages = {x--y}, year = {2023} } ``` -->

References

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

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