Text Classification
Transformers
ONNX
Safetensors
modernbert
semantic-router
vela
text-embeddings-inference
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-Domain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/Vela-1.0-Encoder-307M-Domain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/Vela-1.0-Encoder-307M-Domain")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Domain") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Domain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 625 Bytes
b5b2c74 | 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 | {
"category_to_idx": {
"biology": 0,
"business": 1,
"chemistry": 2,
"computer science": 3,
"economics": 4,
"engineering": 5,
"health": 6,
"history": 7,
"law": 8,
"math": 9,
"other": 10,
"philosophy": 11,
"physics": 12,
"psychology": 13
},
"idx_to_category": {
"0": "biology",
"1": "business",
"2": "chemistry",
"3": "computer science",
"4": "economics",
"5": "engineering",
"6": "health",
"7": "history",
"8": "law",
"9": "math",
"10": "other",
"11": "philosophy",
"12": "physics",
"13": "psychology"
}
}
|