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SimpleTransformers
这个库是基于抱抱脸（HuggingFace）的Transformers库。SimpleTransformers 可以让你快速训练和评估Transformer模型。只需3行代码就可以初始化模型、训练模型和评估模型。它支持Sequence Classification, Token Classification (NER),Question Answering,Language Model Fine-Tuning, Language Model Training, Language Generation, T5 Model, Seq2Seq Tasks , Multi-Modal Classification 和Conversational AI.
Weights & Biases框架
支持Weights和Biases以实现模型训练的可视化。要使用这个功能，只需在
args字典的wandb_project属性中为W&B设置一个项目名称。这将把所有超参数值、训练损失和评估指标记录到给定项目中。1
model = ClassificationModel('roberta', 'roberta-base', args={'wandb_project': 'project-name'})
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任何传入
wandb.init的附加参数都可以作为wandb_kwargs参数传递。 架构
该库被设计为为每个NLP任务设计一个单独的类。提供类似功能的类被分组到一起。
simpletransformers.classification- 包括所有分类模型。ClassificationModelMultiLabelClassificationModel
simpletransformers.ner- 包括所有分类模型。NERModel
simpletransformers.question_answering- 包括所有命名实体识别（Named Entity Recognition）模型。QuestionAnsweringModel
这里有一些简单的例子
MultiLabel Classification
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model = MultiLabelClassificationModel("distilbert","distilbert-base-uncased",num_labels=6, args={"reprocess_input_data": True, "overwrite_output_dir": True, "num_train_epochs":epochs,'learning_rate':learning_rate, 'wandb_project': "simpletransformers"}, ) # Train the model model.train_model(train_df) # Evaluate the model result, model_outputs, wrong_predictions = model.eval_model(eval_df)
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Question Answering
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train_args = { 'learning_rate': wandb.config.learning_rate, 'num_train_epochs': 2, 'max_seq_length': 128, 'doc_stride': 64, 'overwrite_output_dir': True, 'reprocess_input_data': False, 'train_batch_size': 2, 'fp16': False, 'wandb_project': "simpletransformers"}model = QuestionAnsweringModel('distilbert', 'distilbert-base-cased', args=train_args)model.train_model(train_data)
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SimpleTransformers为所有常见的自然语言任务提供了类和训练脚本。以下该库支持的全局参数的完整列表，以及他们的默认参数。
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global_args = { "adam_epsilon": 1e-8, "best_model_dir": "outputs/best_model", "cache_dir": "cache_dir/", "config": {}, "do_lower_case": False, "early_stopping_consider_epochs": False, "early_stopping_delta": 0, "early_stopping_metric": "eval_loss", "early_stopping_metric_minimize": True, "early_stopping_patience": 3, "encoding": None, "eval_batch_size": 8, "evaluate_during_training": False, "evaluate_during_training_silent": True, "evaluate_during_training_steps": 2000, "evaluate_during_training_verbose": False, "fp16": True, "fp16_opt_level": "O1", "gradient_accumulation_steps": 1, "learning_rate": 4e-5, "local_rank": -1, "logging_steps": 50, "manual_seed": None, "max_grad_norm": 1.0, "max_seq_length": 128, "multiprocessing_chunksize": 500, "n_gpu": 1, "no_cache": False, "no_save": False, "num_train_epochs": 1, "output_dir": "outputs/", "overwrite_output_dir": False, "process_count": cpu_count() - 2 if cpu_count() > 2 else 1, "reprocess_input_data": True, "save_best_model": True, "save_eval_checkpoints": True, "save_model_every_epoch": True, "save_steps": 2000, "save_optimizer_and_scheduler": True, "silent": False, "tensorboard_dir": None, "train_batch_size": 8, "use_cached_eval_features": False, "use_early_stopping": False, "use_multiprocessing": True, "wandb_kwargs": {}, "wandb_project": None, "warmup_ratio": 0.06, "warmup_steps": 0, "weight_decay": 0,}
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最近更新 9mo ago