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README.md

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----
-license: apache-2.0
----
+---
+language: en
+tags:
+- emotion-classification
+- multilabel
+- text-classification
+- pytorch
+- transformers
+- deberta-v3-large
+license: apache-2.0
+metrics:
+- f1
+---
+
+# Multilabel Emotion Classification Model (DeBERTa-v3-large)
+
+## Model Description
+This model is fine-tuned DeBERTa-v3-large for multilabel emotion classification. It can predict multiple emotions simultaneously from text with superior performance using disentangled attention mechanisms.
+
+## Emotions Detected
+amusement, anger, annoyance, caring, confusion, disappointment, disgust, embarrassment, excitement, fear, gratitude, joy, love, sadness
+
+## Performance
+- **Macro F1 Score**: 0.3913
+- **Training Data**: 37164 samples
+- **Validation Data**: 9291 samples
+
+## Key Features
+- **Disentangled Attention**: Separates content and position representations
+- **Enhanced Mask Decoder**: Better handling of masked tokens
+- **Relative Position Bias**: Improved positional understanding
+- **Multilabel Capability**: Simultaneous prediction of multiple emotions
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("your-username/emotion-classifier-deberta")
+model = AutoModel.from_pretrained("your-username/emotion-classifier-deberta")
+
+# Example usage
+text = "I'm so happy and excited about this!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.sigmoid(outputs.logits)
+```
+
+## Training Details
+- **Base Model**: microsoft/deberta-v3-base
+- **Training Epochs**: 2
+- **Learning Rate**: 1e-05
+- **Batch Size**: 16
+- **Max Length**: 128
+- **Memory Optimizations**: Gradient accumulation, FP16, gradient checkpointing
+
+## Model Architecture
+- **Total Parameters**: 183,842,318
+- **Trainable Parameters**: 183,842,318
+
+## Training Optimizations
+- Mixed precision training (FP16)
+- Gradient accumulation for memory efficiency
+- Gradient checkpointing
+- Early stopping based on macro F1 score

added_tokens.json

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+{
+  "[MASK]": 128000
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spm.model

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+size 2464616

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128000": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

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