Instructions to use ydshieh/tiny-random-BertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-BertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ydshieh/tiny-random-BertForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-BertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("ydshieh/tiny-random-BertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ydshieh/tiny-random-BertForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 481 kB
-
https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/refs%2Fpr%2F2/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-BertForSequenceClassification@refs/pr/2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/refs%2Fpr%2F2/tf_model.h5
481 kB
- Xet hash:
- b077574e6bd5882cef916f67f5a941b7e120dee8717ad345237a783e5c42f308
- Size of remote file:
- 481 kB
- SHA256:
- 979ba2c9fac51997ac94ef276aba08acd22daf4173b6971f725e4c32f0530b7e
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