Instructions to use Privoid-ai/s61tiny-bert-NLP-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Privoid-ai/s61tiny-bert-NLP-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Privoid-ai/s61tiny-bert-NLP-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Privoid-ai/s61tiny-bert-NLP-model") model = AutoModelForSequenceClassification.from_pretrained("Privoid-ai/s61tiny-bert-NLP-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Privoid-ai/s61tiny-bert-NLP-model: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/Privoid-ai/s61tiny-bert-NLP-model/resolve/main/training_args.bin
- Command line
-
hf download hf://Privoid-ai/s61tiny-bert-NLP-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Privoid-ai/s61tiny-bert-NLP-model/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- e5a9effb85571c133255c5abf9cddbd5955b37622ccdc15703733f3c4a869b69
- Size of remote file:
- 4.86 kB
- SHA256:
- 6552566d51c4c4fc7a5fe36b6f447a6f21f21c4122bfc996541a76b17d9e88ed
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