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%2F7/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-BertForTokenClassification@refs/pr/7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/refs%2Fpr%2F7/pytorch_model.bin
379 kB
- Xet hash:
- 53b983d3627e401db562b9818d1d82192681f1112a7332e96907d543189fab4a
- Size of remote file:
- 379 kB
- SHA256:
- 12616bd6cca468066f392b47d7487d869cbb39fd069f79b2b43ae4b50f095d1e
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