Instructions to use ydshieh/tiny-random-BertForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-BertForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ydshieh/tiny-random-BertForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-BertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("ydshieh/tiny-random-BertForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ydshieh/tiny-random-BertForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 379 kB
-
https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-BertForTokenClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/main/pytorch_model.bin
379 kB
- Xet hash:
- cf675ef5955d7205a58749364bc49e7cf74e22290ba7b3d60b8bbb301512b918
- Size of remote file:
- 379 kB
- SHA256:
- a990fa3dbd35e005e56306fcc049a79886337aeca38fd6d82282776705a4dcbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.