Instructions to use CAMeL-Lab/camelbert-msa-qalb14-ged-13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/camelbert-msa-qalb14-ged-13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CAMeL-Lab/camelbert-msa-qalb14-ged-13")# Load model directly from transformers import AutoTokenizer, BertForTokenClassificationSingleLabel tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/camelbert-msa-qalb14-ged-13") model = BertForTokenClassificationSingleLabel.from_pretrained("CAMeL-Lab/camelbert-msa-qalb14-ged-13") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88e8217cadf2dfe6acbe0d54ce343964787dacbc2dae104289f1e4f86210e1f5
- Size of remote file:
- 434 MB
- SHA256:
- 23385ffe560860a8f5e9e46e05b66a31c33a4b0bd58e9a718933fdab8161f50f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.