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")# pip install -U transformers accelerate # 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 tokenizer_config.json from moshew/MiniLM-L6-clinc-distilled: direct link, hf CLI and curl.
- Browser
- Download file 391 Bytes
-
https://huggingface.co/moshew/MiniLM-L6-clinc-distilled/resolve/main/tokenizer_config.json
- Command line
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hf download hf://moshew/MiniLM-L6-clinc-distilled/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/moshew/MiniLM-L6-clinc-distilled/resolve/main/tokenizer_config.json
391 Bytes
| {"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large", "tokenizer_class": "RobertaTokenizer"} |