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 pytorch_model.bin from ydshieh/tiny-random-BertForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 384 kB
-
https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-BertForSequenceClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-BertForSequenceClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
384 kB
- Xet hash:
- 524ab0782c050bfc9c4ab196a7cf8a9eb0f57e7702f1794bf55af5ab457bbf7f
- Size of remote file:
- 384 kB
- SHA256:
- e3bbca1d8fb2bd25c6162e34563e42fecf7f17197d7ae7173c0b233bbded97e6
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