Instructions to use Babak-Behkamkia/bert_SEMEVAL_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Babak-Behkamkia/bert_SEMEVAL_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Babak-Behkamkia/bert_SEMEVAL_long")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Babak-Behkamkia/bert_SEMEVAL_long") model = AutoModelForSequenceClassification.from_pretrained("Babak-Behkamkia/bert_SEMEVAL_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28ddf0d23a6856c188bfef1268cf49d9024ee22f8ef8b7adefa592f0410309da
- Size of remote file:
- 433 MB
- SHA256:
- 76624b63b5e42745b459d462831b0c2280b335ceda3815370d2b74b56adbfadf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.