Instructions to use hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1: direct link, hf CLI and curl.
- Browser
- Download file 669 kB
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https://huggingface.co/hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1/resolve/main/tokenizer.json
- Command line
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hf download hf://hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/hazyresearch/M2-BERT-2k-Retrieval-Encoder-V1/resolve/main/tokenizer.json
669 kB
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