Text Classification
Transformers
PyTorch
Safetensors
English
sproto
multi-label-classification
long-tail-learning
medical
clinical-nlp
interpretability
prototypical-networks
ehr
custom_code
Instructions to use DATEXIS/sproto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DATEXIS/sproto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DATEXIS/sproto", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DATEXIS/sproto", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_lower_case": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "unk_token": "[UNK]", | |
| "pad_token_id": 0, | |
| "sep_token_id": 102, | |
| "cls_token_id": 101, | |
| "mask_token_id": 103, | |
| "unk_token_id": 100, | |
| "model_max_length": 512 | |
| } |