Instructions to use denritchie/tBERT-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use denritchie/tBERT-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="denritchie/tBERT-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("denritchie/tBERT-v1") model = AutoModelForMaskedLM.from_pretrained("denritchie/tBERT-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from denritchie/tBERT-v1: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/denritchie/tBERT-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://denritchie/tBERT-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/denritchie/tBERT-v1/resolve/main/pytorch_model.bin
336 MB
- Xet hash:
- 9c315dd305caf72c851acc4df9c1dd3d7a8ec4a2d284c999ae0b27deac798ed6
- Size of remote file:
- 336 MB
- SHA256:
- 06b50ff1e0a7c4e8a0fccd6f8de68a46df52627008bb89321963882545d64440
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.