text classification bug fix & support ernie m #3184
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wawltor
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Sep 5, 2022
| @@ -376,7 +384,7 @@ python prune.py \ | |||
| * `per_device_eval_batch_size`：开发集评测过程批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为32。 | |||
| * `learning_rate`：训练最大学习率；默认为3e-5。 | |||
| * `num_train_epochs`: 训练轮次，使用早停法时可以选择100；默认为10。 | |||
| * `logging_steps`: 训练过程中日志打印的间隔steps数，默认5。 | |||
| * `logging_steps`: 训练过程中日志打印的间隔steps数，默认100。 | |||
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这里的logging steps对于大多数cpu用户来说，是不是太大了
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这是因为trainer里默认的参数就是100，但我在训练时命令行的参数还是设置--logging_steps 5
| 'token_type_ids'], batch['labels'] | ||
| logits = model(input_ids, token_type_ids) | ||
| label = batch.pop("labels") | ||
| logits = model(**batch) |
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为了可以适配erniem模型，erniem模型tokenizer得到的数据和模型输入都没有token_type_ids
| 'token_type_ids'], batch['labels'] | ||
| logits = model(input_ids, token_type_ids) | ||
| label = batch.pop("labels") | ||
| logits = model(**batch) |
| @@ -264,16 +267,15 @@ checkpoint/ | |||
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| * 如需恢复模型训练，则可以设置 `init_from_ckpt` ， 如 `init_from_ckpt=checkpoint/model_state.pdparams` 。 | |||
| * 如需训练英文文本分类任务，只需更换预训练模型参数 `model_name` 。英文训练任务推荐使用"ernie-2.0-base-en"，更多可选模型可参考[Transformer预训练模型](https://paddlenlp.readthedocs.io/zh/latest/model_zoo/index.html#transformer)。 | |||
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| * 英文和中文以外文本分类任务建议使用多语言预训练模型"ernie-m-base","ernie-m-large"， 多语言模型暂不支持文本分类模型部署，相关功能正在加速开发中。 | |||
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ernie-m版本目前在适配压缩API，主要问题是什么了？
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erniem模型tokenizer得到的数据和模型输入都没有token_type_ids，但压缩API训练的时候默认模型输入要有token_type_ids
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Description
修复了文本分类中的bug，新增支持ernie-m模型（裁剪和部署部分暂不支持）