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-4/checkpoint-4000/optimizer.pt from YagiASAFAS/MsIssuesBERT: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/YagiASAFAS/MsIssuesBERT/resolve/main/run-4/checkpoint-4000/optimizer.pt
- Command line
-
hf download hf://YagiASAFAS/MsIssuesBERT/run-4/checkpoint-4000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/YagiASAFAS/MsIssuesBERT/resolve/main/run-4/checkpoint-4000/optimizer.pt
876 MB
- Xet hash:
- f19d618448bbf489ed6aea7a990711a9911cc43eea0be92ee8d07db557925b4f
- Size of remote file:
- 876 MB
- SHA256:
- d40344be585ee37464135b83c24195198e2456208a008bb7521f165aa19a8e17
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