Model name: felipesfpaula/bertimbau-large-InferBr-NLI
Base model: neuralmind/bert-large-portuguese-cased
Task: Natural Language Inference (NLI) on Brazilian Portuguese
Dataset: InferBR
- Premise–Hypothesis pairs in Portuguese
- Label mapping:
  - 0 – Contradiction
  - 1 – Entailment
  - 2 – Neutral

Intended Use

This model is intended for research and applications requiring Portuguese NLI, such as:

Automated textual reasoning in Portuguese
Downstream tasks: question answering, summarization consistency checks, semantic search
Academic experiments in Portuguese natural language understanding

Not intended for:

Sensitive decision-making without human oversight
Use on texts in languages other than Brazilian Portuguese

Training Data

Training split: InferBR “train” (premise, hypothesis, label)
Validation split: InferBR “validation”
Test split: InferBR “test”
Preprocessing:
- Tokenized with neuralmind/bert-large-portuguese-cased tokenizer
- Maximum sequence length: 128 tokens
- Padding to max length
- Labels cast to integer IDs {0,1,2}

Training Procedure

Fine-tuned on: neuralmind/bert-large-portuguese-cased
Batch size: 32
Learning rate: 2e-5
Optimizer: AdamW (with default weight decay)
Number of epochs: 10
Evaluation strategy: Evaluate on validation split at end of each epoch
Checkpointing: Saved best model by validation accuracy
Random seed: 42

Evaluation Results (Test Set)

Test accuracy: 0.9395
Test F₁‐macro: 0.7596
- F₁ label 0 (Contradiction): 0.9191
- F₁ label 1 (Entailment): 0.6022
- F₁ label 2 (Neutral): 0.7575

These metrics were computed on the held‐out InferBR test split.

accuracy = (number of correctly predicted labels) / (total number of examples)
f1_macro = unweighted average F₁ across labels {0,1,2}

Limitations

Imbalanced performance: Label 1 (Entailment) has lower F₁ (0.6022), indicating the model sometimes confuses entailment examples.
Domain specificity: Trained on InferBR, which consists of generic NLI pairs. May not generalize to highly specialized or technical domains (e.g., legal, medical).
Language restrictions: Only supports Brazilian Portuguese. Performance on European Portuguese or code‐switched text is not guaranteed.
Bias and fairness: InferBR may contain topics or writing styles that do not cover all registers of Portuguese. Use caution if deploying in production for sensitive tasks.

How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 1. Load tokenizer and model from HuggingFace
tokenizer = AutoTokenizer.from_pretrained("felipesfpaula/bertimbau-large-InferBr-NLI")
model = AutoModelForSequenceClassification.from_pretrained("felipesfpaula/bertimbau-large-InferBr-NLI")

# 2. Encode a premise–hypothesis pair
premise = "O gato está sentado no sofá."
hypothesis = "O gato está deitado no sofá."
encoded = tokenizer(premise, hypothesis, return_tensors="pt", max_length=128, truncation=True, padding="max_length")

# 3. Run inference
with torch.no_grad():
    outputs = model(**encoded)
    logits = outputs.logits
    pred_id = torch.argmax(logits, dim=-1).item()

# 4. Map prediction to label
label_map = {0: "Contradiction", 1: "Entailment", 2: "Neutral"}
print(f"Predicted label: {label_map[pred_id]}")