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--- |
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license: mit |
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tags: |
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- embedding |
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- text-embedding |
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- crypto |
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- nlp |
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library_name: transformers |
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--- |
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# crypto-mini-embed |
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**crypto-mini-embed** adalah contoh model mini embedding berbasis arsitektur sederhana untuk eksperimen NLP seperti: |
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- text similarity |
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- vector search |
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- clustering |
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- semantic tagging |
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- crypto-topic classification |
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Model ini merupakan **dummy model** untuk membantu pengguna memahami struktur repository model di HuggingFace. |
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--- |
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## ⚙️ Arsitektur Model |
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- Tipe model: `MiniEmbeddingModel` |
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- Hidden size: 64 |
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- Max length: 128 tokens |
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- Framework: PyTorch |
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- Format: Safetensors |
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- Tokenizer: Basic CharTokenizer (dummy) |
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--- |
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## 📦 File dalam Model |
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| File | Fungsi | |
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|------|--------| |
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| `config.json` | Konfigurasi model | |
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| `tokenizer.json` | Tokenizer sederhana | |
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| `model.safetensors` | Parameter model | |
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| `README.md` | Dokumentasi model | |
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--- |
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## 🧪 Contoh Penggunaan |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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tok = AutoTokenizer.from_pretrained("0xcubin/crypto-mini-embed") |
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model = AutoModel.from_pretrained("0xcubin/crypto-mini-embed") |
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text = "Bitcoin is digital money" |
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inputs = tok(text, return_tensors="pt") |
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with torch.no_grad(): |
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emb = model(**inputs).last_hidden_state.mean(dim=1) |
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print(emb.shape) # contoh: (1, 64) |
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