File size: 2,234 Bytes
a5235ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # 📥 Download RoBERTa Joint NER+RE Model
The large model files are hosted on HuggingFace Hub for better performance and reliability.
## 🤗 Direct Download from HuggingFace Hub
### Option 1: Using Transformers Library (Recommended)
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
# Automatically downloads and caches the model
tokenizer = AutoTokenizer.from_pretrained("lemkin-ai/roberta-joint-ner-re")
model = AutoModelForTokenClassification.from_pretrained("lemkin-ai/roberta-joint-ner-re")
```
### Option 2: Manual Download via CLI
```bash
# Install HuggingFace Hub CLI
pip install huggingface_hub
# Download all model files
huggingface-cli download lemkin-ai/roberta-joint-ner-re --local-dir ./models/roberta-joint-ner-re/
```
### Option 3: Git Clone from HuggingFace
```bash
# Clone the model repository
git clone https://huggingface.co/lemkin-ai/roberta-joint-ner-re
```
## 📊 Model Files Available on HuggingFace Hub
| File | Size | Description |
|------|------|-------------|
| `pytorch_model.bin` | 2.1GB | PyTorch model weights |
| `config.json` | 2KB | Model configuration |
| `tokenizer_config.json` | 1KB | Tokenizer configuration |
| `vocab.json` | 779KB | Vocabulary file |
| `merges.txt` | 446KB | BPE merges |
| `tokenizer.json` | 2.0MB | Fast tokenizer |
| `special_tokens_map.json` | 1KB | Special tokens mapping |
## 🌐 Model Hub URL
**https://huggingface.co/lemkin-ai/roberta-joint-ner-re**
## ⚡ Quick Start
```python
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification
# Load model
tokenizer = AutoTokenizer.from_pretrained("lemkin-ai/roberta-joint-ner-re")
model = AutoModelForTokenClassification.from_pretrained("lemkin-ai/roberta-joint-ner-re")
# Example usage
text = "The International Criminal Court issued a warrant for the general's arrest."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
# Process predictions for NER + RE tasks
print("Entity predictions:", predictions)
```
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*For complete documentation, see the main repository README and model card on HuggingFace Hub.* |