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 tf_model.h5 from ydshieh/tiny-random-BertForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 474 kB
-
https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/main/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-BertForTokenClassification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/main/tf_model.h5
474 kB
- Xet hash:
- 0581a343c292b589bee36d2e79664cb3fe01d16647109a7e74151ca17692114f
- Size of remote file:
- 474 kB
- SHA256:
- 5db5fafeee555a31ad978790782878cdbc36aea9dbac2a4b448e85938fd0e962
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.