Instructions to use TakalaWang/bert-finetuned-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TakalaWang/bert-finetuned-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TakalaWang/bert-finetuned-balanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TakalaWang/bert-finetuned-balanced") model = AutoModelForSequenceClassification.from_pretrained("TakalaWang/bert-finetuned-balanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from TakalaWang/bert-finetuned-balanced: direct link, hf CLI and curl.
- Browser
- Download file 5.71 kB
-
https://huggingface.co/TakalaWang/bert-finetuned-balanced/resolve/main/training_args.bin
- Command line
-
hf download hf://TakalaWang/bert-finetuned-balanced/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/TakalaWang/bert-finetuned-balanced/resolve/main/training_args.bin
5.71 kB
- Xet hash:
- 679761e0a302ea479658544ce43dabd52b5b5a57ba3e7f885c7717376b82ea5e
- Size of remote file:
- 5.71 kB
- SHA256:
- 67ff0f32e528c2cd3814133974b2b3eb17627fffcfa0e8197950e0beacf3bc00
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