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| from typing import Optional | |
| import pandas as pd | |
| import streamlit as st | |
| from datasets import Dataset # type: ignore | |
| from src.data import encode_dataset, get_collator, get_data, predict | |
| from src.model import get_encoder, get_model, get_tokenizer | |
| from src.subpages import Context | |
| from src.utils import align_sample, device, explode_df | |
| _TOKENIZER_NAME = ( | |
| "xlm-roberta-base", | |
| "gagan3012/bert-tiny-finetuned-ner", | |
| "distilbert-base-german-cased", | |
| )[0] | |
| def _load_models_and_tokenizer( | |
| encoder_model_name: str, | |
| model_name: str, | |
| tokenizer_name: Optional[str], | |
| device: str = "cpu", | |
| ): | |
| sentence_encoder = get_encoder(encoder_model_name, device=device) | |
| tokenizer = get_tokenizer(tokenizer_name if tokenizer_name else model_name) | |
| labels = "O B-COMMA".split() if "comma" in model_name else None | |
| model = get_model(model_name, labels=labels) | |
| return sentence_encoder, model, tokenizer | |
| def load_context( | |
| encoder_model_name: str, | |
| model_name: str, | |
| ds_name: str, | |
| ds_config_name: str, | |
| ds_split_name: str, | |
| split_sample_size: int, | |
| randomize_sample: bool, | |
| **kw_args, | |
| ) -> Context: | |
| """Utility method loading (almost) everything we need for the application. | |
| This exists just because we want to cache the results of this function. | |
| Args: | |
| encoder_model_name (str): Name of the sentence encoder to load. | |
| model_name (str): Name of the NER model to load. | |
| ds_name (str): Dataset name or path. | |
| ds_config_name (str): Dataset config name. | |
| ds_split_name (str): Dataset split name. | |
| split_sample_size (int): Number of examples to load from the split. | |
| Returns: | |
| Context: An object containing everything we need for the application. | |
| """ | |
| sentence_encoder, model, tokenizer = _load_models_and_tokenizer( | |
| encoder_model_name=encoder_model_name, | |
| model_name=model_name, | |
| tokenizer_name=_TOKENIZER_NAME if "comma" in model_name else None, | |
| device=str(device), | |
| ) | |
| collator = get_collator(tokenizer) | |
| # load data related stuff | |
| split: Dataset = get_data( | |
| ds_name, ds_config_name, ds_split_name, split_sample_size, randomize_sample | |
| ) | |
| tags = split.features["ner_tags"].feature | |
| split_encoded, word_ids, ids = encode_dataset(split, tokenizer) | |
| # transform into dataframe | |
| df = predict(split_encoded, model, tokenizer, collator, tags) | |
| df["word_ids"] = word_ids | |
| df["ids"] = ids | |
| # explode, clean, merge | |
| df_tokens = explode_df(df) | |
| df_tokens_cleaned = df_tokens.query("labels != 'IGN'") | |
| df_merged = pd.DataFrame(df.apply(align_sample, axis=1).tolist()) | |
| df_tokens_merged = explode_df(df_merged) | |
| return Context( | |
| **{ | |
| "model": model, | |
| "tokenizer": tokenizer, | |
| "sentence_encoder": sentence_encoder, | |
| "df": df, | |
| "df_tokens": df_tokens, | |
| "df_tokens_cleaned": df_tokens_cleaned, | |
| "df_tokens_merged": df_tokens_merged, | |
| "tags": tags, | |
| "labels": tags.names, | |
| "split_sample_size": split_sample_size, | |
| "ds_name": ds_name, | |
| "ds_config_name": ds_config_name, | |
| "ds_split_name": ds_split_name, | |
| "split": split, | |
| } | |
| ) | |