Instructions to use tiagoblima/nilc-binary-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiagoblima/nilc-binary-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiagoblima/nilc-binary-bert-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiagoblima/nilc-binary-bert-large") model = AutoModelForSequenceClassification.from_pretrained("tiagoblima/nilc-binary-bert-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tiagoblima/nilc-binary-bert-large: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/tiagoblima/nilc-binary-bert-large/resolve/main/training_args.bin
- Command line
-
hf download hf://tiagoblima/nilc-binary-bert-large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tiagoblima/nilc-binary-bert-large/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 521f0bccdc8d25e179889fb5e95a81489d70dc92160e2436f8ea193fcf4b1c6d
- Size of remote file:
- 3.52 kB
- SHA256:
- ee47572b584b88cdf05caec59449a0efcf220690788ee9d9345e55df3a779e39
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