Feature Extraction
Transformers
ONNX
Safetensors
bert
sentence-embeddings
retrieval
legal
swiss-law
cross-lingual
multilingual-e5
text-embeddings-inference
Instructions to use franciscomira/fedlex-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franciscomira/fedlex-e5-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="franciscomira/fedlex-e5-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("franciscomira/fedlex-e5-small") model = AutoModel.from_pretrained("franciscomira/fedlex-e5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from franciscomira/fedlex-e5-small: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/franciscomira/fedlex-e5-small/resolve/main/tokenizer.json
- Command line
-
hf download hf://franciscomira/fedlex-e5-small/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/franciscomira/fedlex-e5-small/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5421f4fdf05332faa8d9243f89cae47e88d686358ac86b85c82f2066e00e6927
- Size of remote file:
- 17.1 MB
- SHA256:
- 5b58593a7e63f3c8b69801a92415fc0813dd287a3ba2f767cdf33106806fca11
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