Upgrade applications/text_classifications/multi_class to use Trainer API #3679
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ZeyuChen
reviewed
Nov 5, 2022
| - `device`: 使用的设备，默认为`gpu`。 | ||
| - `per_device_train_batch_size`: 每次训练每张卡上的样本数量。可根据实际GPU显存适当调小/调大此配置。 | ||
| - `per_device_eval_batch_size`: 每次评估每张卡上的样本数量。可根据实际GPU显存适当调小/调大此配置。 | ||
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找不到save_strategy的参数说明，整体检查下使用的配置是否有欠缺
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因为支持了Trainer的原因，增加了大量可使用的参数. 所以这里只是说明了一些用户可能调整的主要参数。剩下的参数在文档中链接了TrainingArguments的参数文档，供用户查阅
lugimzzz
reviewed
Nov 15, 2022
| --metric_for_best_model accuracy \ | ||
| --load_best_model_at_end \ | ||
| --evaluation_strategy epoch \ | ||
| --save_strategy epoch |
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新增一个可配置参数--save_total_limit 1,限制保存的epoch数
lugimzzz
reviewed
Nov 16, 2022
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Addressed comments from @lugimzzz |
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PR types
Function optimization
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Description
Upgrade applications/text_classifications/multi_class to use Trainer API