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 pytorch_model.bin from tiagoblima/nilc-binary-bert-large: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/tiagoblima/nilc-binary-bert-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiagoblima/nilc-binary-bert-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiagoblima/nilc-binary-bert-large/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 1e188d072f19f4b6818495f2bca9993caa6834a898fd0e512727f6ef3f9fb1f7
- Size of remote file:
- 1.34 GB
- SHA256:
- 2c30f81e955275907d8824ee2dec4c168949303e7e0004169b678addb70e87f3
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