Instructions to use A-Funakoshi/sample-text-classification-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use A-Funakoshi/sample-text-classification-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="A-Funakoshi/sample-text-classification-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("A-Funakoshi/sample-text-classification-bert") model = AutoModelForSequenceClassification.from_pretrained("A-Funakoshi/sample-text-classification-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from A-Funakoshi/sample-text-classification-bert: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/A-Funakoshi/sample-text-classification-bert/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://A-Funakoshi/sample-text-classification-bert@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/A-Funakoshi/sample-text-classification-bert/resolve/refs%2Fpr%2F1/pytorch_model.bin
443 MB
- Xet hash:
- b03d18b14b259c5326299ddb2aba0bac35447ea6cda6f037bd4afb8c1268f44a
- Size of remote file:
- 443 MB
- SHA256:
- efaeeb76dc6ec51e0d40e3fadf0538b5b83825be30faf9c74f8bc0b525c9a146
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