Instructions to use Data-Lab/multilingual-e5-small_classification_v0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Lab/multilingual-e5-small_classification_v0.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Data-Lab/multilingual-e5-small_classification_v0.4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Data-Lab/multilingual-e5-small_classification_v0.4") model = AutoModelForSequenceClassification.from_pretrained("Data-Lab/multilingual-e5-small_classification_v0.4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Data-Lab/multilingual-e5-small_classification_v0.4: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/Data-Lab/multilingual-e5-small_classification_v0.4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Data-Lab/multilingual-e5-small_classification_v0.4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Data-Lab/multilingual-e5-small_classification_v0.4/resolve/main/pytorch_model.bin
471 MB
- Xet hash:
- 01edd4ebaf023338863ea0fd606a9419c5b4f00d4a3be24b72f9f008aad9c535
- Size of remote file:
- 471 MB
- SHA256:
- dec01ec31166516a2cbbb5ba323473d07e082088f552afc22b6ab642620cd9dd
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