Instructions to use mmoradi/Robust-Biomed-RoBERTa-TextualInference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmoradi/Robust-Biomed-RoBERTa-TextualInference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mmoradi/Robust-Biomed-RoBERTa-TextualInference")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mmoradi/Robust-Biomed-RoBERTa-TextualInference") model = AutoModel.from_pretrained("mmoradi/Robust-Biomed-RoBERTa-TextualInference") - Notebooks
- Google Colab
- Kaggle
Upload prediction_head_0_config.json
Browse files
prediction_head_0_config.json
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{"training": false, "layer_dims": [768, 3], "num_labels": 3, "ph_output_type": "per_sequence", "model_type": "multilabel_text_classification", "task_name": "textual_inference", "class_weights": null, "pred_threshold": 0.5, "balanced_weights": null, "config": {"training": true, "layer_dims": [768, 3], "num_labels": 3, "ph_output_type": "per_sequence", "model_type": "multilabel_text_classification", "task_name": "textual_inference", "class_weights": null, "pred_threshold": 0.5, "balanced_weights": null, "name": "MultiLabelTextClassificationHead"}, "label_tensor_name": "textual_inference_label_ids", "label_list": ["entailment", "contradiction", "neutral"], "metric": "acc", "name": "MultiLabelTextClassificationHead"}
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