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| import torch | |
| from transformers import AutoTokenizer | |
| from extended_embeddings.token_classification import ExtendedEmbeddigsRobertaForTokenClassification | |
| from data_manipulation.dataset_funcions import load_gazetteers, gazetteer_matching, align_gazetteers_with_tokens | |
| from data_manipulation.preprocess_gazetteers import build_reverse_dictionary | |
| def load(): | |
| model_name = "ufal/robeczech-base" | |
| model_path = "bettystr/NerRoB-czech" | |
| model = ExtendedEmbeddigsRobertaForTokenClassification.from_pretrained(model_path).to("cpu") | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model.eval() | |
| gazetteers_path = "gazz2.json" | |
| gazetteers_for_matching = load_gazetteers(gazetteers_path) | |
| temp = [] | |
| for i in gazetteers_for_matching.keys(): | |
| temp.append(build_reverse_dictionary({i: gazetteers_for_matching[i]})) | |
| gazetteers_for_matching = temp | |
| return tokenizer, model, gazetteers_for_matching | |
| def run(tokenizer, model, gazetteers_for_matching, text): | |
| tokenized_inputs = tokenizer( | |
| text, truncation=True, is_split_into_words=False, return_offsets_mapping=True | |
| ) | |
| matches = gazetteer_matching(text, gazetteers_for_matching) | |
| new_g = [] | |
| word_ids = tokenized_inputs.word_ids() | |
| new_g.append(align_gazetteers_with_tokens(matches, word_ids)) | |
| p, o, l = [], [], [] | |
| for i in new_g: | |
| p.append([x[0] for x in i]) | |
| o.append([x[1] for x in i]) | |
| l.append([x[2] for x in i]) | |
| input_ids = torch.tensor(tokenized_inputs["input_ids"], device="cpu").unsqueeze(0) | |
| attention_mask = torch.tensor(tokenized_inputs["attention_mask"], device="cpu").unsqueeze(0) | |
| per = torch.tensor(p, device="cpu") | |
| org = torch.tensor(o, device="cpu") | |
| loc = torch.tensor(l, device="cpu") | |
| output = model(input_ids=input_ids, attention_mask=attention_mask, per=per, org=org, loc=loc).logits | |
| predictions = torch.argmax(output, dim=2).tolist() | |
| predicted_tags = [[model.config.id2label[idx] for idx in sentence] for sentence in predictions] | |
| softmax = torch.nn.Softmax(dim=2) | |
| scores = softmax(output).squeeze(0).tolist() | |
| result = [] | |
| temp = { | |
| "start": 0, | |
| "end": 0, | |
| "entity": "O", | |
| "score": 0, | |
| "word": "", | |
| "count": 0 | |
| } | |
| for pos, entity, score in zip(tokenized_inputs.offset_mapping, predicted_tags[0], scores): | |
| if pos[0] == pos[1] or entity == "O": | |
| continue | |
| if temp["entity"] == entity[2:]: # same entity | |
| space = " " if pos[0] - temp["end"] >= 1 else "" | |
| temp["end"] = pos[1] | |
| temp["word"] += space + text[pos[0]:pos[1]] | |
| temp["count"] += 1 | |
| temp["score"] += max(score) | |
| else: # new entity | |
| if temp["count"] > 0: | |
| temp["score"] /= temp.pop("count") | |
| result.append(temp) | |
| temp = { | |
| "start": pos[0], | |
| "end": pos[1], | |
| "entity": entity[2:], | |
| "score": 0, | |
| "word": text[pos[0]:pos[1]], | |
| "count": 1 | |
| } | |
| if temp["count"] > 0: | |
| temp["score"] /= temp.pop("count") | |
| result.append(temp) | |
| return result | |