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")# 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
- Xet hash:
- 80c9bbdd6678462f26340206c4265515075aa71b75c2a0379ae38077dbdfcc96
- Size of remote file:
- 5.78 kB
- SHA256:
- a4ad559f091ae7757869f5224f652bb49e6e3989ffd03fbb0eefdb9252513c40
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.