Instructions to use mmoradi/Robust-Biomed-RoBERTa-RelationClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmoradi/Robust-Biomed-RoBERTa-RelationClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mmoradi/Robust-Biomed-RoBERTa-RelationClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mmoradi/Robust-Biomed-RoBERTa-RelationClassification") model = AutoModel.from_pretrained("mmoradi/Robust-Biomed-RoBERTa-RelationClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 703 Bytes
650b4dd | 1 | {"baskets": [], "data_dir": "BioText-Data", "delimiter": "\t", "dev_filename": null, "dev_split": 0, "header": 0, "max_samples": null, "max_seq_len": 128, "proxies": null, "quote_char": "'", "skiprows": null, "tasks": {"text_classification": {"label_list": ["NONE", "DISONLY", "TREATONLY", "PREVENT", "VAGUE", "TO_SEE", "TREAT_FOR_DIS", "SIDE_EFF", "TREAT_NO_FOR_DIS"], "metric": "acc", "label_tensor_name": "relation_classification_label_ids", "label_name": "relation_classification_label", "label_column_name": "label", "task_type": "multilabel_classification"}}, "test_filename": "Test.tsv", "train_filename": "Train.tsv", "tokenizer": "BertTokenizer", "processor": "RelationClassificationProcessor"} |