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README.md

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+---
+license: mit
+language:
+  - en
+library_name: transformers
+tags:
+  - emoji
+  - text-classification
+  - sentiment
+  - deberta
+  - deberta-v3
+  - emoji-prediction
+datasets:
+  - custom
+base_model: microsoft/deberta-v3-small
+pipeline_tag: text-classification
+metrics:
+  - accuracy
+model-index:
+  - name: bertmoji-deberta-v3-small
+    results:
+      - task:
+          type: text-classification
+          name: Emoji Prediction
+        metrics:
+          - type: accuracy
+            value: 0.9019
+            name: Validation Accuracy
+          - type: accuracy
+            value: 0.9761
+            name: Top-3 Accuracy
+---
+
+# BertMoji: Emoji Prediction with DeBERTa-v3
+
+BertMoji predicts the most appropriate emoji for a given text message. Built on DeBERTa-v3-small, it classifies text into 250 emoji categories with 90.2% accuracy.
+
+## Model Description
+
+- **Base Model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small)
+- **Task:** Multi-class emoji classification (250 classes)
+- **Architecture:** DeBERTa-v3 encoder + classification head
+- **Training:** Fine-tuned on ~23,500 synthetic text-emoji pairs. The model was refined over several fine-tuning sessions and evaluations.
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Validation Accuracy | **90.2%** |
+| Top-3 Accuracy | **97.6%** |
+| Number of Classes | 250 |
+
+## Quick Start
+
+```python
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, DebertaV2Model
+import json
+
+class BertmojiClassifier(nn.Module):
+    def __init__(self, model_name, num_classes):
+        super().__init__()
+        self.encoder = DebertaV2Model.from_pretrained(model_name)
+        hidden_size = self.encoder.config.hidden_size
+        self.classifier = nn.Sequential(
+            nn.Dropout(0.1),
+            nn.Linear(hidden_size, hidden_size),
+            nn.GELU(),
+            nn.Dropout(0.1),
+            nn.Linear(hidden_size, num_classes)
+        )
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        pooled = outputs.last_hidden_state[:, 0, :]
+        return self.classifier(pooled)
+
+# Load model
+model_path = "your-username/bertmoji-deberta-v3-small"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+with open(f"{model_path}/emoji_mappings.json") as f:
+    mappings = json.load(f)
+id_to_emoji = {int(k): v for k, v in mappings['id_to_emoji'].items()}
+
+model = BertmojiClassifier("microsoft/deberta-v3-small", len(id_to_emoji))
+model.load_state_dict(torch.load(f"{model_path}/pytorch_model.bin", map_location="cpu"))
+model.eval()
+
+# Predict
+def predict_emoji(text, top_k=3):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=96)
+    with torch.no_grad():
+        logits = model(inputs["input_ids"], inputs["attention_mask"])
+        probs = torch.softmax(logits, dim=-1)
+        top_probs, top_ids = probs.topk(top_k)
+    return [(id_to_emoji[idx.item()], prob.item()) for idx, prob in zip(top_ids[0], top_probs[0])]
+
+# Example
+print(predict_emoji("This pizza is absolutely incredible"))
+# Output: [('pizza_emoji', 0.98), ...]
+```
+
+## Demo Examples
+
+| Message | Top-1 | Top-2 | Top-3 |
+|---------|-------|-------|-------|
+| "I got the promotion! All those late nights finally paid off" | 😴 25% | 😃 12% | ✈️ 10% |
+| "Done with finals! Time to sleep for three days straight" | 😴 51% | ☔ 17% | ✈️ 10% |
+| "My little one turns 5 today! Where did the time go" | 🎂 41% | 🐶 6% | 🐕 6% |
+| "New personal record on deadlifts this morning" | 🏋️ 72% | 🎊 8% | 💼 3% |
+| "You're going to crush that interview! Believe in yourself" | 💪 55% | 💅 19% | ✨ 7% |
+| "This pizza is absolutely incredible" | 🍕 98% | 🍔 1% | 🍽️ 0% |
+| "Look at this adorable face! My puppy is the cutest" | 🐶 80% | 🐱 9% | 🐕 3% |
+| "Cheers to the weekend! We earned this" | 🥂 92% | 🍾 2% | 🎂 1% |
+| "Off to Tokyo! Can't wait to explore" | ✈️ 58% | ⛰️ 16% | 🏋️ 4% |
+| "What a goal! My team is on fire tonight" | 💪 61% | ✨ 8% | 💅 5% |
+| "So grateful for my amazing team. Couldn't do it without you all" | 💙 44% | 💕 14% | ✊ 7% |
+| "Rainy day, hot coffee, good book. Perfect Sunday" | ☔ 65% | 🚗 25% | ❄️ 2% |
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | microsoft/deberta-v3-small |
+| Hidden Size | 768 |
+| Max Sequence Length | 96 |
+| Batch Size | 32 |
+| Learning Rate | 2e-6 |
+| Optimizer | AdamW |
+| Training Samples | ~23,500 |
+
+## Limitations
+
+- Trained on synthetic English text; may not generalize to all languages or dialects
+- Some emoji categories have limited training data
+- Model reflects biases present in training data generation
+
+## License
+
+MIT License
+
+## Citation
+
+```bibtex
+@misc{bertmoji2024,
+  title={BertMoji: Emoji Prediction with DeBERTa-v3},
+  author={Mitchell Currie},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/your-username/bertmoji-deberta-v3-small}
+}
+```

added_tokens.json

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

config.json

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+{
+  "return_dict": true,
+  "output_hidden_states": false,
+  "torchscript": false,
+  "dtype": null,
+  "pruned_heads": {},
+  "tie_word_embeddings": true,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "architectures": [
+    "BertmojiClassifier"
+  ],
+  "finetuning_task": null,
+  "id2label": {
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emoji_mappings.json

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+    "212": "🤙",
+    "213": "🤚",
+    "214": "🤛",
+    "215": "🤜",
+    "216": "🤝",
+    "217": "🤞",
+    "218": "🤟",
+    "219": "🤠",
+    "220": "🤡",
+    "221": "🤢",
+    "222": "🤤",
+    "223": "🤥",
+    "224": "🤧",
+    "225": "🤨",
+    "226": "🤩",
+    "227": "🤪",
+    "228": "🤫",
+    "229": "🤬",
+    "230": "🤭",
+    "231": "🤮",
+    "232": "🤯",
+    "233": "🤲",
+    "234": "🤳",
+    "235": "🥂",
+    "236": "🥗",
+    "237": "🥰",
+    "238": "🥱",
+    "239": "🥲",
+    "240": "🥳",
+    "241": "🥴",
+    "242": "🥵",
+    "243": "🥶",
+    "244": "🥸",
+    "245": "🥺",
+    "246": "🧐",
+    "247": "🧢",
+    "248": "🧳",
+    "249": "🫶"
+  }
+}

labels.txt

@@ -0,0 +1,250 @@
+⌨️
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model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03f68b05ecf38866f36f399fd64b6475d66e8fb07fc6ffd3783f18c2f0192252
+size 568361384

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c35bfb4d4885c4edff9cee02d3b3dcce68075b7cd494cfa32c3f54035927d42
+size 568392395

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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+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "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"
+}