Instructions to use tkuye/tiny-bert-jdc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkuye/tiny-bert-jdc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tkuye/tiny-bert-jdc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tkuye/tiny-bert-jdc") model = AutoModelForSequenceClassification.from_pretrained("tkuye/tiny-bert-jdc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tkuye/tiny-bert-jdc: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/tkuye/tiny-bert-jdc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tkuye/tiny-bert-jdc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tkuye/tiny-bert-jdc/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 1c3e7e0f141a9994d53a6fb610aa63f48dceca1235c3887f3331b408f915c02e
- Size of remote file:
- 17.6 MB
- SHA256:
- 81f169ec9375ada38d7ceecfa633cdbeece57a362b1fd9e515a2bd97cf4f998e
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