Instructions to use ACCORD-NLP/re-bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ACCORD-NLP/re-bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ACCORD-NLP/re-bert-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ACCORD-NLP/re-bert-base-cased") model = AutoModelForSequenceClassification.from_pretrained("ACCORD-NLP/re-bert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ACCORD-NLP/re-bert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 862 MB
-
https://huggingface.co/ACCORD-NLP/re-bert-base-cased/resolve/main/optimizer.pt
- Command line
-
hf download hf://ACCORD-NLP/re-bert-base-cased/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ACCORD-NLP/re-bert-base-cased/resolve/main/optimizer.pt
862 MB
- Xet hash:
- b670f21dcb038c687c97157ec89dd08f42b06a0c6b8a1ba58ea53a8fa7f4e724
- Size of remote file:
- 862 MB
- SHA256:
- 34b277048e0b456909b3757213c13ac9ac10928483f3b1b8c078d2934e7d5254
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