felipesfpaula
/

bertimbau-large-InferBr-NLI

@@ -1,199 +1,93 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: mit
+datasets:
+- hapaxlegomenon/InferBR
+language:
+- pt
+base_model:
+- neuralmind/bert-large-portuguese-cased
 ---
+# Model Card: BERT-Large-Portuguese-Cased Fine-Tuned on InferBR NLI
 ## Model Details
+- **Model name:** `felipesfpaula/bertimbau-large-InferBr-NLI`
+- **Base model:** `neuralmind/bert-large-portuguese-cased`
+- **Task:** Natural Language Inference (NLI) on Brazilian Portuguese
+- **Dataset:** [InferBR](https://huggingface.co/datasets/hapaxlegomenon/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]}")