Instructions to use malteos/aspect-acl-scibert-scivocab-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malteos/aspect-acl-scibert-scivocab-uncased with Transformers:
# Load model directly from transformers import AutoTokenizer, BertForMultiLabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("malteos/aspect-acl-scibert-scivocab-uncased") model = BertForMultiLabelSequenceClassification.from_pretrained("malteos/aspect-acl-scibert-scivocab-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fe0c31d6db9f4054158edaf3f5e07daba9ddb99cd505c32a8094032e0bb6387
- Size of remote file:
- 440 MB
- SHA256:
- 676fd2b295d44187f01160d8bffbe92758ecc2790178b031a50e84f1b91b80d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.