Instructions to use moshew/MiniLM-L6-clinc-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moshew/MiniLM-L6-clinc-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moshew/MiniLM-L6-clinc-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moshew/MiniLM-L6-clinc-distilled") model = AutoModelForSequenceClassification.from_pretrained("moshew/MiniLM-L6-clinc-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moshew/MiniLM-L6-clinc-distilled: direct link, hf CLI and curl.
- Browser
- Download file 121 MB
-
https://huggingface.co/moshew/MiniLM-L6-clinc-distilled/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://moshew/MiniLM-L6-clinc-distilled/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/moshew/MiniLM-L6-clinc-distilled/resolve/main/pytorch_model.bin
121 MB
- Xet hash:
- 74c47b812f669c949b9f48c092ed5b6d5d870ed9bae0408770d152306a8b6045
- Size of remote file:
- 121 MB
- SHA256:
- 092d529c6a847ca9a5464e5b3dc982af6932aa4ddddc27bf7635856362669138
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