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")# pip install -U transformers accelerate # 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 training_args.bin from moshew/MiniLM-L3-clinc-distilled: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/moshew/MiniLM-L3-clinc-distilled/resolve/main/training_args.bin
- Command line
-
hf download hf://moshew/MiniLM-L3-clinc-distilled/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/moshew/MiniLM-L3-clinc-distilled/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 3cecef5c17ac56623c933c8d5d9437e1c1f535ba585e2d53d94364bd2cf15abd
- Size of remote file:
- 3.12 kB
- SHA256:
- 89137abf20ade4d52354d92e5a7519824fb94a2f832680aa0aa329de382aee4f
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