Instructions to use Babak-Behkamkia/bert_mydataset_VAST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Babak-Behkamkia/bert_mydataset_VAST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Babak-Behkamkia/bert_mydataset_VAST")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Babak-Behkamkia/bert_mydataset_VAST") model = AutoModelForSequenceClassification.from_pretrained("Babak-Behkamkia/bert_mydataset_VAST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6dbf9b7a6b003e7baa17effdb0cb0b299989186aa384e8fb9751d95f1fe71f0f
- Size of remote file:
- 433 MB
- SHA256:
- fccf81cee5b4327b8228897c77061415f7994905812dbdd9c15891241cb76688
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.