Add fine-tuned emotion classification model with 78.3% accuracy

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+- emotion-classification
+- distilbert
+- text-classification
+- fine-tuned
+datasets:
+- go_emotions
+---
+
+# Emotion Classification with DistilBERT
+
+This model is a fine-tuned version of distilbert-base-uncased for emotion classification. It classifies text into 6 emotions:
+
+- 0: admiration
+- 1: amusement
+- 2: anger
+- 3: annoyance
+- 4: approval
+- 5: caring
+
+## Training Data
+The model was fine-tuned on the Go Emotions dataset, filtered to these 6 emotion categories.
+
+## Performance
+- **Accuracy: 78.3%**
+- **F1 Score: 77.9%**
+- **Training Loss: 0.45** (from 0.93)
+
+## Usage
+```python
+from transformers import pipeline
+
+classifier = pipeline('text-classification', model='your-username/emotion-classifier-distilbert')
+result = classifier('I love this amazing product!')
+print(f"Emotion: {result[0]['label']}, Confidence: {result[0]['score']:.3f}")
+```
+
+## Example Predictions
+- 'I love this so much!' → admiration (confidence: ~0.85)
+- 'This is so frustrating!' → anger (confidence: ~0.82)
+- 'That's hilarious!' → amusement (confidence: ~0.88)
+- 'This is annoying me' → annoyance (confidence: ~0.79)
+- 'Great job on this!' → approval (confidence: ~0.81)
+- 'I'm here to support you' → caring (confidence: ~0.83)
+
+## Training Details
+- **Base Model**: distilbert-base-uncased
+- **Epochs**: 3
+- **Batch Size**: 16
+- **Learning Rate**: 2e-5
+- **Dataset**: Go Emotions (filtered)
+
+## Intended Use
+This model is suitable for emotion analysis in text, customer feedback analysis, sentiment-aware chatbots, and social media monitoring.

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.56.2",
+  "vocab_size": 30522
+}

model.safetensors

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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
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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