Instructions to use Danieljava/minilm-language-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Danieljava/minilm-language-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Danieljava/minilm-language-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Danieljava/minilm-language-classifier") model = AutoModelForSequenceClassification.from_pretrained("Danieljava/minilm-language-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Danieljava/minilm-language-classifier: direct link, hf CLI and curl.
- Browser
- Download file 133 MB
-
https://huggingface.co/Danieljava/minilm-language-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://Danieljava/minilm-language-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Danieljava/minilm-language-classifier/resolve/main/model.safetensors
133 MB
- Xet hash:
- a48930fc5fafe4833350cfff90019d44a712a71a09e37f91abc4d47aa3b908cd
- Size of remote file:
- 133 MB
- SHA256:
- bd16ac2bfde898b19d0949325b9efa814db586470c41af3b4a23c1c62b81988e
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