Instructions to use vocab-transformers/distilbert-word2vec_256k-MLM_1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vocab-transformers/distilbert-word2vec_256k-MLM_1M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vocab-transformers/distilbert-word2vec_256k-MLM_1M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vocab-transformers/distilbert-word2vec_256k-MLM_1M") model = AutoModelForMaskedLM.from_pretrained("vocab-transformers/distilbert-word2vec_256k-MLM_1M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vocab-transformers/distilbert-word2vec_256k-MLM_1M: direct link, hf CLI and curl.
- Browser
- Download file 962 MB
-
https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vocab-transformers/distilbert-word2vec_256k-MLM_1M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/resolve/main/pytorch_model.bin
962 MB
- Xet hash:
- 709c1b18d75212a7a28725d3ccd63168b5947f37e0c9f5ab3f774db88172d86f
- Size of remote file:
- 962 MB
- SHA256:
- da48b857b925b6aa9920aeb9abe7a482c9452d92e0a1856f72bd050cff46f63c
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