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/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-BertForTokenClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
379 kB
- Xet hash:
- 730ee778a43dfb7724a2048423325aa02a23379330dc14ce158a2b8f92b5d32c
- Size of remote file:
- 379 kB
- SHA256:
- fb1e0dc22bf0b2cc1412f74d48c4c75218ac8432304c09ec54aef4927d9d86be
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