Instructions to use moshew/MiniLM-L3-clinc-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moshew/MiniLM-L3-clinc-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moshew/MiniLM-L3-clinc-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moshew/MiniLM-L3-clinc-distilled") model = AutoModelForSequenceClassification.from_pretrained("moshew/MiniLM-L3-clinc-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moshew/MiniLM-L3-clinc-distilled: direct link, hf CLI and curl.
- Browser
- Download file 69.8 MB
-
https://huggingface.co/moshew/MiniLM-L3-clinc-distilled/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://moshew/MiniLM-L3-clinc-distilled/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/moshew/MiniLM-L3-clinc-distilled/resolve/main/pytorch_model.bin
69.8 MB
- Xet hash:
- 73c88f3c88e0eeddbe639bd9e1b5ed63222f6f01fbd51f71210fbe20a96693a8
- Size of remote file:
- 69.8 MB
- SHA256:
- 4a82241a795c1fedeb5ff6b9e1d99adffa0627f20337753774c552292452bddf
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