To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. To do so, we first introduce a pretraining task that is designed to be used for fine-tuning. This approach allows us to use the fine-tuning data as input in the fine-tuning task. We then use the fine-tuning task as input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us to use the fine-tuning data as input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us to use theFine-tuning data as input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us. to use the fine-tuning data as input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. The same approach can be used for fine-tuning tasks as well. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us to use the fine-tuning data as input in the Fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us to use the fine-tuning data as a input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us to use the fine-fitting data as input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allows us to use all of the fine-tuning data as input in the fine-tuning task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-tuning, we use a pretraining task that is designed to be used for fine-tuning. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-fitting, we use a pretraining task that is designed to be used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-fitting, we use a pretraining task that is designed to being used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-fitting, we use a pretraining tool that is designed to be used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case of fine-fitting,we use a pretraining tool that is designed to be used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In the case if no fine-fitting data are available, we use a pretraining tool that is designed to be used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In these two cases, we use a pretraining tool that is designed to be used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data. In both cases, we use a pretraining tool that is designed to be used for fine-fitting. This approach allowed us to use all of the fine-fitting data as input in the fine-fitting task. To tackle this issue, prompt tuning (Lester et al., 2021), prefix tuning (Li and Liang, 2021), and P-tuning (Liu et al., 2021a,b) approaches propose to preprocess training data.