Delete loading script auxiliary file

lm_finetuning.py

@@ -1,250 +0,0 @@
-'''
-wandb offline
-export WANDB_DISABLED='true'
-export RAY_RESULTS='ray_results'
-
-python lm_finetuning.py -m "roberta-large" -o "ckpt/2021/roberta-large" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-large-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
-python lm_finetuning.py -m "roberta-large" -o "ckpt/2020/roberta-large" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-large-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
-
-python lm_finetuning.py -m "roberta-base" -o "ckpt/2021/roberta_base" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-base-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
-python lm_finetuning.py -m "roberta-base" -o "ckpt/2020/roberta_base" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-base-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
-
-python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-2019-90m" -o "ckpt/2021/twitter-roberta-base-2019-90m" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
-python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-2019-90m" -o "ckpt/2020/twitter-roberta-base-2019-90m" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
-
-python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2020" -o "ckpt/2021/twitter-roberta-base-dec2020" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2020-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
-python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2020" -o "ckpt/2020/twitter-roberta-base-dec2020" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2020-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
-
-python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2021" -o "ckpt/2021/twitter-roberta-base-dec2021" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2021-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
-python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2021" -o "ckpt/2020/twitter-roberta-base-dec2021" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2021-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
-'''
-
-import argparse
-import json
-import logging
-import os
-import math
-import shutil
-import urllib.request
-import multiprocessing
-from os.path import join as pj
-
-import torch
-import numpy as np
-from huggingface_hub import create_repo
-from datasets import load_dataset, load_metric
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
-from ray import tune
-
-from readme import get_readme
-
-
-logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')
-
-PARALLEL = bool(int(os.getenv("PARALLEL", 1)))
-RAY_RESULTS = os.getenv("RAY_RESULTS", "ray_results")
-LABEL2ID = {
-    "arts_&_culture": 0,
-    "business_&_entrepreneurs": 1,
-    "celebrity_&_pop_culture": 2,
-    "diaries_&_daily_life": 3,
-    "family": 4,
-    "fashion_&_style": 5,
-    "film_tv_&_video": 6,
-    "fitness_&_health": 7,
-    "food_&_dining": 8,
-    "gaming": 9,
-    "learning_&_educational": 10,
-    "music": 11,
-    "news_&_social_concern": 12,
-    "other_hobbies": 13,
-    "relationships": 14,
-    "science_&_technology": 15,
-    "sports": 16,
-    "travel_&_adventure": 17,
-    "youth_&_student_life": 18
-}
-ID2LABEL = {v: k for k, v in LABEL2ID.items()}
-
-
-def internet_connection(host='http://google.com'):
-    try:
-        urllib.request.urlopen(host)
-        return True
-    except:
-        return False
-
-
-def sigmoid(x):
-  return 1 / (1 + math.exp(-x))
-
-
-def get_metrics():
-    metric_accuracy = load_metric("accuracy", "multilabel")
-    metric_f1 = load_metric("f1", "multilabel")
-
-    # metric_f1.compute(predictions=[[0, 1, 1], [1, 1, 0]], references=[[0, 1, 1], [0, 1, 0]], average='micro')
-    # metric_accuracy.compute(predictions=[[0, 1, 1], [1, 1, 0]], references=[[0, 1, 1], [0, 1, 0]])
-
-    def compute_metric_search(eval_pred):
-        logits, labels = eval_pred
-        predictions = np.array([[int(sigmoid(j) > 0.5) for j in i] for i in logits])
-        return metric_f1.compute(predictions=predictions, references=labels, average='micro')
-
-    def compute_metric_all(eval_pred):
-        logits, labels = eval_pred
-        predictions = np.array([[int(sigmoid(j) > 0.5) for j in i] for i in logits])
-        return {
-            'f1': metric_f1.compute(predictions=predictions, references=labels, average='micro')['f1'],
-            'f1_macro': metric_f1.compute(predictions=predictions, references=labels, average='macro')['f1'],
-            'accuracy': metric_accuracy.compute(predictions=predictions, references=labels)['accuracy']
-        }
-    return compute_metric_search, compute_metric_all
-
-
-def main():
-    parser = argparse.ArgumentParser(description='Fine-tuning language model.')
-    parser.add_argument('-m', '--model', help='transformer LM', default='roberta-base', type=str)
-    parser.add_argument('-d', '--dataset', help='', default='cardiffnlp/tweet_topic_multi', type=str)
-    parser.add_argument('--split-train', help='', required=True, type=str)
-    parser.add_argument('--split-validation', help='', required=True, type=str)
-    parser.add_argument('--split-test', help='', required=True, type=str)
-    parser.add_argument('-l', '--seq-length', help='', default=128, type=int)
-    parser.add_argument('--random-seed', help='', default=42, type=int)
-    parser.add_argument('--eval-step', help='', default=50, type=int)
-    parser.add_argument('-o', '--output-dir', help='Directory to output', default='ckpt_tmp', type=str)
-    parser.add_argument('-t', '--n-trials', default=10, type=int)
-    parser.add_argument('--push-to-hub', action='store_true')
-    parser.add_argument('--use-auth-token', action='store_true')
-    parser.add_argument('--hf-organization', default=None, type=str)
-    parser.add_argument('-a', '--model-alias', help='', default=None, type=str)
-    parser.add_argument('--summary-file', default='metric_summary.json', type=str)
-    parser.add_argument('--skip-train', action='store_true')
-    parser.add_argument('--skip-eval', action='store_true')
-    opt = parser.parse_args()
-    assert opt.summary_file.endswith('.json'), f'`--summary-file` should be a json file {opt.summary_file}'
-    # setup data
-    dataset = load_dataset(opt.dataset)
-    network = internet_connection()
-    # setup model
-    tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network)
-    model = AutoModelForSequenceClassification.from_pretrained(
-        opt.model,
-        id2label=ID2LABEL,
-        label2id=LABEL2ID,
-        num_labels=len(dataset[opt.split_train]['label'][0]),
-        local_files_only=not network,
-        problem_type="multi_label_classification"
-    )
-    tokenized_datasets = dataset.map(
-        lambda x: tokenizer(x["text"], padding="max_length", truncation=True, max_length=opt.seq_length),
-        batched=True)
-    # setup metrics
-    compute_metric_search, compute_metric_all = get_metrics()
-
-    if not opt.skip_train:
-        # setup trainer
-        trainer = Trainer(
-            model=model,
-            args=TrainingArguments(
-                output_dir=opt.output_dir,
-                evaluation_strategy="steps",
-                eval_steps=opt.eval_step,
-                seed=opt.random_seed
-            ),
-            train_dataset=tokenized_datasets[opt.split_train],
-            eval_dataset=tokenized_datasets[opt.split_validation],
-            compute_metrics=compute_metric_search,
-            model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(
-                opt.model,
-                return_dict=True,
-                num_labels=len(dataset[opt.split_train]['label'][0]),
-                id2label=ID2LABEL,
-                label2id=LABEL2ID
-            )
-        )
-        # parameter search
-        if PARALLEL:
-            best_run = trainer.hyperparameter_search(
-                hp_space=lambda x: {
-                    "learning_rate": tune.loguniform(1e-6, 1e-4),
-                    "num_train_epochs": tune.choice(list(range(1, 6))),
-                    "per_device_train_batch_size": tune.choice([4, 8, 16, 32, 64]),
-                },
-                local_dir=RAY_RESULTS, direction="maximize", backend="ray", n_trials=opt.n_trials,
-                resources_per_trial={'cpu': multiprocessing.cpu_count(), "gpu": torch.cuda.device_count()},
-
-            )
-        else:
-            best_run = trainer.hyperparameter_search(
-                hp_space=lambda x: {
-                    "learning_rate": tune.loguniform(1e-6, 1e-4),
-                    "num_train_epochs": tune.choice(list(range(1, 6))),
-                    "per_device_train_batch_size": tune.choice([4, 8, 16, 32, 64]),
-                },
-                local_dir=RAY_RESULTS, direction="maximize", backend="ray", n_trials=opt.n_trials
-            )
-        # finetuning
-        for n, v in best_run.hyperparameters.items():
-            setattr(trainer.args, n, v)
-        trainer.train()
-        trainer.save_model(pj(opt.output_dir, 'best_model'))
-        best_model_path = pj(opt.output_dir, 'best_model')
-    else:
-        best_model_path = pj(opt.output_dir, 'best_model')
-
-    # evaluation
-    model = AutoModelForSequenceClassification.from_pretrained(
-        best_model_path,
-        num_labels=len(dataset[opt.split_train]['label'][0]),
-        local_files_only=not network,
-        problem_type="multi_label_classification",
-        id2label=ID2LABEL,
-        label2id=LABEL2ID
-    )
-    trainer = Trainer(
-        model=model,
-        args=TrainingArguments(
-            output_dir=opt.output_dir,
-            evaluation_strategy="no",
-            seed=opt.random_seed
-        ),
-        train_dataset=tokenized_datasets[opt.split_train],
-        eval_dataset=tokenized_datasets[opt.split_test],
-        compute_metrics=compute_metric_all
-    )
-    summary_file = pj(opt.output_dir, opt.summary_file)
-    if not opt.skip_eval:
-        result = {f'test/{k}': v for k, v in trainer.evaluate().items()}
-        logging.info(json.dumps(result, indent=4))
-        with open(summary_file, 'w') as f:
-            json.dump(result, f)
-
-    if opt.push_to_hub:
-        assert opt.hf_organization is not None, f'specify hf organization `--hf-organization`'
-        assert opt.model_alias is not None, f'specify hf organization `--model-alias`'
-        url = create_repo(opt.model_alias, organization=opt.hf_organization, exist_ok=True)
-        # if not opt.skip_train:
-        args = {"use_auth_token": opt.use_auth_token, "repo_url": url, "organization": opt.hf_organization}
-        trainer.model.push_to_hub(opt.model_alias, **args)
-        tokenizer.push_to_hub(opt.model_alias, **args)
-        if os.path.exists(summary_file):
-            shutil.copy2(summary_file, opt.model_alias)
-        extra_desc = f"This model is fine-tuned on `{opt.split_train}` split and validated on `{opt.split_test}` split of tweet_topic."
-        readme = get_readme(
-            model_name=f"{opt.hf_organization}/{opt.model_alias}",
-            metric=summary_file,
-            language_model=opt.model,
-            extra_desc= extra_desc
-            )
-        with open(f"{opt.model_alias}/README.md", "w") as f:
-            f.write(readme)
-        os.system(
-            f"cd {opt.model_alias} && git lfs install && git add . && git commit -m 'model update' && git push && cd ../")
-        shutil.rmtree(f"{opt.model_alias}")  # clean up the cloned repo
-
-
-if __name__ == '__main__':
-    main()
-
-