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 training_args.bin from A-Funakoshi/sample-text-classification-bert: direct link, hf CLI and curl.
- Browser
- Download file 4.02 kB
-
https://huggingface.co/A-Funakoshi/sample-text-classification-bert/resolve/main/training_args.bin
- Command line
-
hf download hf://A-Funakoshi/sample-text-classification-bert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/A-Funakoshi/sample-text-classification-bert/resolve/main/training_args.bin
4.02 kB
- Xet hash:
- 7fe01c50156e45874bc3c98fe56280116d03604765e21cc1457d0c105e342765
- Size of remote file:
- 4.02 kB
- SHA256:
- 90919965fa8d550dd2517104570f80e2ff56984cbd40408d9c74c012ffed307d
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