py310-pylingual-v1-segmenter
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py310-pylingual-v1-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py311-pylingual-v1-segmenter
syssec-utdThis model is a fine-tuned version of syssec-utd/py311-pylingual-v1-mlm on the syssec-utd/segmentation-py311-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0046 - Precision: 0.9958 - Recall: 0.9937 - F1: 0.9948 - Accuracy: 0.9983 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
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View details ↗py311-pylingual-v1-statement
syssec-utdThis model is a fine-tuned version of Salesforce/codet5-base on the syssec-utd/statement-py311-pylingual-v1-tokenized dataset. The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 48 - total_eval_batch_size: 48 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
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View details ↗py312-pylingual-v1-segmenter
syssec-utdThis model is a fine-tuned version of syssec-utd/py312-pylingual-v1-mlm on the syssec-utd/segmentation-py312-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0053 - Precision: 0.9923 - Recall: 0.9935 - F1: 0.9929 - Accuracy: 0.9982 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
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View details ↗py312-pylingual-v1-statement
syssec-utdThis model is a fine-tuned version of Salesforce/codet5-base on the syssec-utd/statement-py312-pylingual-v1-tokenized dataset. The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
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View details ↗py313-pylingual-v1.1-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py313-pylingual-v1.3-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py313-pylingual-v3-segmenter
syssec-utdThis model is a fine-tuned version of syssec-utd/py313-pylingual-v3-mlm on the syssec-utd/segmentation-py313-pylingual-v3-tokenized dataset. It achieves the following results on the evaluation set: - Loss: 0.0047 - Precision: 0.9945 - Recall: 0.9944 - F1: 0.9945 - Accuracy: 0.9983 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 28 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
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View details ↗py313-pylingual-v3-statement
syssec-utdThis model is a fine-tuned version of Salesforce/codet5-base on the syssec-utd/statement-py313-pylingual-v3-tokenized dataset. The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - distributed_type: multi-GPU - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP
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View details ↗py314-pylingual-v10-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py314-pylingual-v10-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py314-pylingual-v4-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py314-pylingual-v4-statement
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View details ↗py314-pylingual-v9-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py314-pylingual-v9-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py315-pylingual-v2-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py315-pylingual-v2-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py36-pylingual-v1-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py36-pylingual-v1-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py37-pylingual-v1-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py37-pylingual-v1-statement
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View details ↗py38-pylingual-v1.3-statement
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗py38-pylingual-v1-segmenter
syssec-utd基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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