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| import datasets | |
| import numpy as np | |
| import torch | |
| import transformers | |
| from config import epochs, batch_size, learning_rate | |
| from model import tokenizer, multitask_model | |
| from mtm import MultitaskTrainer, NLPDataCollator, DataLoaderWithTaskname | |
| import pandas as pd | |
| from datasets import Dataset, DatasetDict | |
| from data_predict import convert_to_stsb_features,convert_to_features | |
| from huggingface_hub import hf_hub_download,snapshot_download | |
| # features_dict = {} | |
| # extra_feature_dict = {} | |
| # sentinews_location = "" | |
| # df_document_croatian_test = pd.read_csv(sentinews_location+"textlabel.tsv", sep="\t") | |
| # df_document_croatian_test = df_document_croatian_test[["content"]] | |
| def predict(): | |
| # gather everyone if you want to have a single DatasetDict | |
| document = DatasetDict({ | |
| # "train": Dataset.from_pandas(df_document_sl_hr_train), | |
| # "valid": Dataset.from_pandas(df_document_sl_hr_valid), | |
| "test": Dataset.from_dict({"content":["Volim ti"]}) | |
| }) | |
| dataset_dict = { | |
| "document": document, | |
| } | |
| for task_name, dataset in dataset_dict.items(): | |
| print(task_name) | |
| print(dataset_dict[task_name]["test"][0]) | |
| print() | |
| convert_func_dict = { | |
| "document": convert_to_stsb_features, | |
| # "paragraph": convert_to_stsb_features, | |
| # "sentence": convert_to_stsb_features, | |
| } | |
| features_dict = convert_to_features(dataset_dict, convert_func_dict) | |
| return features_dict | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| #model_link = snapshot_download(repo_id="FFZG-cleopatra/Croatian-News-Classifier") | |
| model_link = hf_hub_download(repo_id="FFZG-cleopatra/Croatian-News-Classifier",filename = "pytorch_model.bin") | |
| # multitask_model.from_pretrained(, config="/media/gaurish/angela/projects/CroatianSlovenEnglishBert/i-got-u-brother-cleopatra-workshop/src/models/multitask_model_3ep/config.json") | |
| multitask_model.load_state_dict(torch.load(model_link, map_location=device)) | |
| # multitask_model.to(device) | |
| predictions = [] | |
| features_dict = predict() | |
| for _, batch in enumerate(features_dict["document"]['test']): | |
| for key, value in batch.items(): | |
| batch[key] = batch[key].to(device) | |
| task_model = multitask_model.get_model("document") | |
| classifier_output = task_model.forward( | |
| torch.unsqueeze(batch["input_ids"], 0), | |
| torch.unsqueeze(batch["attention_mask"], 0),) | |
| print(tokenizer.decode(batch["input_ids"],skip_special_tokens=True)) | |
| prediction =torch.max(classifier_output.logits, axis=1) | |
| predictions.append(prediction.indices.item()) | |
| pd.DataFrame({"original_predictions":predictions}).to_csv("eacl_slavic.tsv") | |
| trainer = MultitaskTrainer( | |
| model=multitask_model, | |
| args=transformers.TrainingArguments( | |
| learning_rate=learning_rate, | |
| output_dir="/tmp", | |
| do_train=False, | |
| do_eval=True, | |
| # evaluation_strategy ="steps", | |
| # num_train_epochs=epochs, | |
| # fp16=True, | |
| # Adjust batch size if this doesn't fit on the Colab GPU | |
| per_device_train_batch_size=batch_size, | |
| per_device_eval_batch_size=batch_size, | |
| save_steps=3000, | |
| # eval_steps=50, | |
| load_best_model_at_end=True, | |
| ), | |
| data_collator=NLPDataCollator(tokenizer=tokenizer), | |
| callbacks=[], | |
| ) | |
| print(features_dict["document"]["test"]) | |
| tests_dict = {} | |
| for task_name in ["document"]: # "paragraph", "sentence" | |
| test_dataloader = DataLoaderWithTaskname( | |
| task_name, | |
| trainer.get_eval_dataloader(features_dict[task_name]["test"]) | |
| ) | |
| print(len(trainer.get_eval_dataloader(features_dict[task_name]["test"]))) | |
| print(test_dataloader.data_loader.collate_fn) | |
| print(len(test_dataloader.data_loader)) | |
| tests_dict[task_name] = trainer.prediction_loop( | |
| test_dataloader, | |
| description=f"Testing: {task_name}" | |
| ) | |
| print(tests_dict) | |
| for task_name in ["document", ]: #"paragraph","sentence" | |
| for metric in ["precision", "recall", "f1"]: | |
| print("test {} {}:".format(metric, task_name), | |
| datasets.load_metric(metric, | |
| name="dev {} {}".format(metric, task_name)).compute( | |
| predictions=np.argmax( | |
| tests_dict[task_name].predictions, axis=1), | |
| references=tests_dict[task_name].label_ids, average="macro" | |
| )) | |
| print() | |