Instructions to use YagiASAFAS/MsIssuesBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YagiASAFAS/MsIssuesBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YagiASAFAS/MsIssuesBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YagiASAFAS/MsIssuesBERT") model = AutoModelForSequenceClassification.from_pretrained("YagiASAFAS/MsIssuesBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run-3/checkpoint-1000/optimizer.pt from YagiASAFAS/MsIssuesBERT: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/YagiASAFAS/MsIssuesBERT/resolve/main/run-3/checkpoint-1000/optimizer.pt
- Command line
-
hf download hf://YagiASAFAS/MsIssuesBERT/run-3/checkpoint-1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/YagiASAFAS/MsIssuesBERT/resolve/main/run-3/checkpoint-1000/optimizer.pt
876 MB
- Xet hash:
- c8a20b14f5ac5f16f5840331d5a29499fa9c5bf5b4dd29a3b9697ef89966c282
- Size of remote file:
- 876 MB
- SHA256:
- 5f21ab1728b19af20c5a4a48b15ecd67613ad32453eafd3d58f3b11d7ead57bb
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