Upload BERT-RTE-LinearClassifier for EEE 486/586 Assignment

README.md

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+---
+language: en
+license: mit
+datasets:
+  - glue/rte
+tags:
+  - text-classification
+  - glue
+  - bert
+  - recognizing textual entailment
+  - assignment
+metrics:
+  - accuracy
+---
+
+# BERT-RTE Linear Classifier for EEE 486/586 Assignment
+
+This model is a fine-tuned version of `bert-base-uncased` on the RTE (Recognizing Textual Entailment) task from the GLUE benchmark. It was developed as part of the EEE 486/586 Statistical Foundations of Natural Language Processing course assignment.
+
+## Model Architecture
+
+Unlike the standard BERT classification approach, this model implements a custom architecture:
+
+- Uses BERT base model as the encoder for feature extraction
+- Replaces the standard single linear classification head with **multiple linear layers**:
+  - First expansion layer: hidden_size → hidden_size*2
+  - Intermediate layer with ReLU activation and dropout
+  - Final classification layer
+- Uses label smoothing of 0.1 in the loss function for better generalization
+
+## Performance
+
+The model achieves **70.40%** accuracy on the RTE validation set, with the following training dynamics:
+- Best validation accuracy: 70.40% (epoch 3)
+- Final validation accuracy: 69.68% (with early stopping)
+
+## Hyperparameters
+
+The model was optimized using Optuna hyperparameter search:
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Learning rate | 1.72e-05 |
+| Max sequence length | 128 |
+| Dropout rate | 0.2 |
+| Hidden size multiplier | 2 |
+| Weight decay | 0.04 |
+| Batch size | 16 |
+| Training epochs | 6 (+2 for final model) |
+
+## Usage
+
+This model can be used for textual entailment classification (determining whether one text logically follows from another):
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("gal-lardo/BERT-RTE-LinearClassifier")
+model = AutoModelForSequenceClassification.from_pretrained("gal-lardo/BERT-RTE-LinearClassifier")
+
+# Prepare input texts
+premise = "The woman is sleeping on the couch."
+hypothesis = "There is a woman resting."
+
+# Tokenize and predict
+inputs = tokenizer(premise, hypothesis, return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(-1).item()
+
+# Convert prediction to label
+label = "entailment" if prediction == 1 else "not_entailment"
+print(f"Prediction: {label}")
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": 0.2,
+  "custom_params": {
+    "batch_size": 16,
+    "hidden_size_multiplier": 2,
+    "learning_rate": 1.7166350301570613e-05,
+    "max_sequence_length": 128,
+    "weight_decay": 0.04
+  },
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "not_entailment",
+    "1": "entailment"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "entailment": 1,
+    "not_entailment": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+oid sha256:3861415043fdcb6fa8f2c6a7a2810b8f5c2191001fefa5164f5b1673c9d42789
+size 442743563

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}