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81a8a6e a3864e2 81a8a6e a3864e2 81a8a6e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import numpy as np
import transformers
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer, AutoTokenizer
from transformers import DataCollatorForTokenClassification
from datasets import load_dataset, load_metric
import os
from pathlib import Path
import datasets
from datasets import DownloadConfig
import os
from pathlib import Path
from datasets import load_dataset, ClassLabel, DownloadConfig
from transformers import AutoTokenizer
from hf_tokenize import HFTokenizer
from hf_dataset import JPNDataset
metric = load_metric("seqeval")
logger = datasets.logging.get_logger(__name__)
def compute_metrics(p, label_list):
predictions, labels = p
predictions = np.argmax(predictions, axis=2)
# Remove ignored index (special tokens)
true_predictions = [
[label_list[p] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
true_labels = [
[label_list[l] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
results = metric.compute(predictions=true_predictions, references=true_labels)
return {
"precision": results["overall_precision"],
"recall": results["overall_recall"],
"f1": results["overall_f1"],
"accuracy": results["overall_accuracy"],
}
if __name__ == "__main__":
model_n_version = "jpn202401"
max_epochs = 150
learning_rate = 2e-5
batch_size = 12
model_root_dir = "."
hf_pretrained_model_checkpoint = "distilbert-base-uncased"
hf_pretrained_tokenizer_checkpoint = "distilbert-base-uncased"
hf_dataset = JPNDataset()
hf_preprocessor = HFTokenizer.init_vf(hf_pretrained_tokenizer_checkpoint=hf_pretrained_tokenizer_checkpoint)
hf_model = AutoModelForTokenClassification.from_pretrained(hf_pretrained_model_checkpoint,
num_labels=len(hf_dataset.labels))
hf_model.config.id2label = hf_dataset.id2label
hf_model.config.label2id = hf_dataset.label2id
tokenized_datasets = hf_dataset.dataset.map(hf_preprocessor.tokenize_and_align_labels, batched=True)
# ---------------------------------------------------------------------------------------------------
args = TrainingArguments(
f"jpn202401",
evaluation_strategy="epoch",
learning_rate=learning_rate,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=max_epochs,
weight_decay=0.01,
)
data_collator = DataCollatorForTokenClassification(hf_preprocessor.tokenizer)
trainer = Trainer(
hf_model,
args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
tokenizer=hf_preprocessor.tokenizer,
compute_metrics=lambda p: compute_metrics(p=p, label_list=hf_dataset.labels)
)
trainer.train()
trainer.evaluate()
# Predictions on test dataset and evaluation
predictions, labels, _ = trainer.predict(tokenized_datasets["test"])
predictions = np.argmax(predictions, axis=2)
# Remove ignored index (special tokens)
true_predictions = [
[hf_dataset.labels[p] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
true_labels = [
[hf_dataset.labels[l] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
results = metric.compute(predictions=true_predictions, references=true_labels)
print(results)
out_dir = os.path.expanduser(model_root_dir) + "/" + model_n_version
trainer.save_model(out_dir)
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