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/refs%2Fpr%2F5/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-BertForTokenClassification@refs/pr/5/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-BertForTokenClassification/resolve/refs%2Fpr%2F5/tf_model.h5
474 kB
- Xet hash:
- 702b02da270f8bb754eaa981fbe2a555a2bd8652d13bf967fe4ba068b7d8eb58
- Size of remote file:
- 474 kB
- SHA256:
- 3e07fdec74b1c7cf566d1f28b3089b6cd58f69b9e53fa5f16201460edee6f834
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