| from pathlib import Path |
| import torch |
| import numpy as np |
|
|
| from .tokenizer import load_tokenizer, preprocessing_text |
| from .model import load_model |
|
|
| |
| NUM_VARAIBLES = 3 |
| NUM_LABELS = 3 |
| num_labels = NUM_LABELS * NUM_VARAIBLES |
| divice = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| num_labels = NUM_LABELS * NUM_VARAIBLES |
| model_name = "pysentimiento/robertuito-sentiment-analysis" |
|
|
| checkpoint_path = Path(__file__).parent.parent / "checkpoints" / "model.ckpt" |
| tokenizer = load_tokenizer(model_name) |
| model = load_model(checkpoint_path, model_name, num_labels, divice) |
|
|
|
|
| RETURN_VALUES =[ |
| "target_sentiment_negative", |
| "target_sentiment_neutral", |
| "target_sentiment_positive", |
| "companies_sentiment_negative", |
| "companies_sentiment_neutral", |
| "companies_sentiment_positive", |
| "consumers_sentiment_negative", |
| "consumers_sentiment_neutral", |
| "consumers_sentiment_positive" |
| ] |
|
|
| def filter(preds, threshold=0.5): |
| bool = preds > threshold |
| indices = np.where(bool)[0] |
| filtered_values = {RETURN_VALUES[index]: preds[index] for index in indices} |
| return filtered_values |
|
|
| def get_predict(text): |
| inputs = preprocessing_text(text, tokenizer) |
| input_ids = inputs["input_ids"].to(divice) |
| attention_mask = inputs["attention_mask"].to(divice) |
| token_type_ids = inputs["token_type_ids"].to(divice) |
| outputs = model(input_ids, attention_mask, token_type_ids) |
| preds = torch.sigmoid(outputs).detach().cpu().numpy() |
| preds = filter(preds[0]) |
| return preds |
|
|
