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/main/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-BertForSequenceClassification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/main/tf_model.h5
481 kB
- Xet hash:
- 245e48814f2b6ee83819c48bd0d685ec0f9ee2072fba0de5eb130542deb5ef49
- Size of remote file:
- 481 kB
- SHA256:
- b667929ee722cf918b8d3911a3440f26476dec96081d4f20a81b7c413151c346
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