RaTE NER Deberta EU AI Act Compliance Profile

RaTE-NER-Deberta

This model is a fine-tuned version of DeBERTa on the RaTE-NER dataset.

Model description

This model is trained to serve the RaTEScore metric, if you are interested in our pipeline, please refer to our paper and Github.

This model also can be used to extract Abnormality, Non-Abnormality, Anatomy, Disease, Non-Disease in medical radiology reports.

Usage

<details> <summary> Click to expand the usage of this model. </summary> <pre><code> from transformers import AutoTokenizer, AutoModelForTokenClassification import torch def post_process(tokenized_text, predicted_entities, tokenizer): entity_spans = [] start = end = None entity_type = None for i, (token, label) in enumerate(zip(tokenized_text, predicted_entities[:len(tokenized_text)])): if token in ["[CLS]", "[SEP]"]: continue if label != "O" and i < len(predicted_entities) - 1: if label.startswith("B-") and predicted_entities[i+1].startswith("I-"): start = i entity_type = label[2:] elif label.startswith("B-") and predicted_entities[i+1].startswith("B-"): start = i end = i entity_spans.append((start, end, label[2:])) start = i entity_type = label[2:] elif label.startswith("B-") and predicted_entities[i+1].startswith("O"): start = i end = i entity_spans.append((start, end, label[2:])) start = end = None entity_type = None elif label.startswith("I-") and predicted_entities[i+1].startswith("B-"): end = i if start is not None: entity_spans.append((start, end

Angelakeke's obligations as provider

These are Angelakeke's responsibility, not yours.

Provider obligations are being compiled.

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