Instructions to use hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM: direct link, hf CLI and curl.
- Browser
- Download file 377 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM/resolve/main/pytorch_model.bin
377 kB
- Xet hash:
- dbaaddc43cc2a351c6dc1720204800172aeb56c278c68cba6a7fa7e1fc23e487
- Size of remote file:
- 377 kB
- SHA256:
- 2ddeec4d35b6fafef074c5afd3d815a08a0e4588cb35857615a44c401eea9f8a
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