Instructions to use aychang/bert-base-cased-trec-coarse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aychang/bert-base-cased-trec-coarse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aychang/bert-base-cased-trec-coarse")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aychang/bert-base-cased-trec-coarse") model = AutoModelForSequenceClassification.from_pretrained("aychang/bert-base-cased-trec-coarse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aychang/bert-base-cased-trec-coarse: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/aychang/bert-base-cased-trec-coarse/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://aychang/bert-base-cased-trec-coarse@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aychang/bert-base-cased-trec-coarse/resolve/refs%2Fpr%2F2/pytorch_model.bin
433 MB
- Xet hash:
- 79208bd0d1dd3bafd9428aa8ff4412eedc3e34a556188ba0cd295b4eefcccbe6
- Size of remote file:
- 433 MB
- SHA256:
- 73e4a6056f0f7791050689e0216f48bc568c3371b25074f3b1a720be71ea0357
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