import streamlit as st from datasets import load_dataset from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from time import time import torch def load_tok_and_data(lan): st_time = time() tokenizer = AutoTokenizer.from_pretrained("Babelscape/mrebel-large", tgt_lang="tp_XX") tokenizer._src_lang = _Tokens[lan] tokenizer.cur_lang_code_id = tokenizer.convert_tokens_to_ids(_Tokens[lan]) tokenizer.set_src_lang_special_tokens(_Tokens[lan]) dataset = load_dataset('Babelscape/SREDFM', lan, split="test", streaming=True, trust_remote_code=True) dataset = [example for example in dataset.take(1001)] return (tokenizer, dataset) @st.cache_resource def load_model(): st_time = time() print("+++++ loading Model", time() - st_time) model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-large") if torch.cuda.is_available(): _ = model.to("cuda:0") # comment if no GPU available _ = model.eval() print("+++++ loaded model", time() - st_time) return model def extract_triplets_typed(text): triplets = [] relation = '' text = text.strip() current = 'x' subject, relation, object_, object_type, subject_type = '','','','','' for token in text.replace("", "").replace("", "").replace("", "").replace("tp_XX", "").replace("__en__", "").split(): if token == "" or token == "": current = 't' if relation != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) relation = '' subject = '' elif token.startswith("<") and token.endswith(">"): if current == 't' or current == 'o': current = 's' if relation != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) object_ = '' subject_type = token[1:-1] else: current = 'o' object_type = token[1:-1] relation = '' else: if current == 't': subject += ' ' + token elif current == 's': object_ += ' ' + token elif current == 'o': relation += ' ' + token if subject != '' and relation != '' and object_ != '' and object_type != '' and subject_type != '': triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type}) return triplets st.markdown("""This is a demo for the ACL 2023 paper [RED$^{FM}$: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). The pre-trained model is able to extract triplets for up to 400 relation types from Wikidata or be used in downstream Relation Extraction task by fine-tuning. Find the model card [here](https://huggingface.co/Babelscape/mrebel-large). Read more about it in the [paper](https://arxiv.org/abs/2306.09802) and in the original [repository](https://github.com/Babelscape/rebel#REDFM).""") model = load_model() lan = st.selectbox( 'Select a Language', ('ar', 'ca', 'de', 'el', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'nl', 'pl', 'pt', 'ru', 'sv', 'vi', 'zh'), index=1) _Tokens = {'en': 'en_XX', 'de': 'de_DE', 'ca': 'ca_XX', 'ar': 'ar_AR', 'el': 'el_EL', 'es': 'es_XX', 'it': 'it_IT', 'ja': 'ja_XX', 'ko': 'ko_KR', 'hi': 'hi_IN', 'pt': 'pt_XX', 'ru': 'ru_RU', 'pl': 'pl_PL', 'zh': 'zh_CN', 'fr': 'fr_XX', 'vi': 'vi_VN', 'sv':'sv_SE'} tokenizer, dataset = load_tok_and_data(lan) agree = st.checkbox('Free input', False) if agree: text = st.text_input('Input text (current example in catalan)', 'Els Red Hot Chili Peppers es van formar a Los Angeles per Kiedis, Flea, el guitarrista Hillel Slovak i el bateria Jack Irons.') print(text) else: dataset_example = st.slider('dataset id', 0, 1000, 0) text = dataset[dataset_example]['text'] length_penalty = st.slider('length_penalty', 0, 10, 1) num_beams = st.slider('num_beams', 1, 20, 3) num_return_sequences = st.slider('num_return_sequences', 1, num_beams, 2) gen_kwargs = { "max_length": 256, "length_penalty": length_penalty, "num_beams": num_beams, "num_return_sequences": num_return_sequences, "forced_bos_token_id": None, } model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt') generated_tokens = model.generate( model_inputs["input_ids"].to(model.device), attention_mask=model_inputs["attention_mask"].to(model.device), decoder_start_token_id = tokenizer.convert_tokens_to_ids("tp_XX"), **gen_kwargs, ) decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False) st.title('Input text') st.write(text) if not agree: st.title('Silver output') entities = dataset[dataset_example]['entities'] relations =[] for trip in dataset[dataset_example]['relations']: relations.append({'subject': entities[trip['subject']], 'predicate': trip['predicate'], 'object': entities[trip['object']]}) st.write(relations) st.title('Prediction text') decoded_preds = [text.replace('', '').replace('', '').replace('', '') for text in decoded_preds] st.write(decoded_preds) for idx, sentence in enumerate(decoded_preds): st.title(f'Prediction triplets sentence {idx}') st.write(extract_triplets_typed(sentence))