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@@ -40,7 +40,7 @@ python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
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  --vocab_path models/google_zh_vocab.txt \
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  --dataset_path cluecorpussmall_bart_seq512_dataset.pt \
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  --processes_num 32 --seq_length 512 \
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- --target bart
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  ```
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  ```
@@ -51,10 +51,7 @@ python3 pretrain.py --dataset_path cluecorpussmall_bart_seq512_dataset.pt \
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  --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
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  --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
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  --learning_rate 1e-4 --batch_size 16 \
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- --span_masking --span_max_length 3 \
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- --embedding word_pos --tgt_embedding word_pos \
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- --encoder transformer --mask fully_visible --decoder transformer \
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- --target bart --tie_weights --has_lmtarget_bias
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  ```
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  Finally, we convert the pre-trained model into Huggingface's format:
 
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  --vocab_path models/google_zh_vocab.txt \
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  --dataset_path cluecorpussmall_bart_seq512_dataset.pt \
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  --processes_num 32 --seq_length 512 \
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+ --data_processor bart
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  ```
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  ```
 
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  --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
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  --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
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  --learning_rate 1e-4 --batch_size 16 \
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+ --span_masking --span_max_length 3
 
 
 
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  ```
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  Finally, we convert the pre-trained model into Huggingface's format: