For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Related tags

Deep Learning SciBERTSUM
Overview

LongScientificFormer

For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Some codes are borrowed from ONMT(https://github.com/OpenNMT/OpenNMT-py)

Data Preparation

Option 1: download the processed data

Pre-processed data

Put all files into raw_data directory

Step 2. Download Stanford CoreNLP

We will need Stanford CoreNLP to tokenize the data. Download it here and unzip it. Then add the following command to your bash_profile:

export CLASSPATH=/path/to/stanford-corenlp-4.2.2/stanford-corenlp-4.2.2.jar

replacing /path/to/ with the path to where you saved the stanford-corenlp-4.2.2 directory.

step 3. extracting sections from GROBID XML files

python preprocess.py -mode extract_pdf_sections -log_file ../logs/extract_section.log

step 4. extracting text from TIKA XML files

python preprocess.py -mode get_text_clean_tika -log_file ../logs/extract_tika_text.log

step 5. Tokenize texts from papers and slides using stanfordCoreNLP

python preprocess.py -mode tokenize  -save_path ../temp -log_file ../logs/tokenize_by_corenlp.log

Step 6. Extract source, section, and target from tokenized files

python preprocess.py -mode clean_paper_jsons -save_path ../json_data/  -n_cpus 10 -log_file ../logs/build_json.log

Step 7. Generate BERT .pt files from source, sections and targets

python preprocess.py -mode format_to_bert -raw_path ../json_data/ -save_path ../bert_data  -lower -n_cpus 40 -log_file ../logs/build_bert_files.log

Model Training

First run: For the first time, you should use single-GPU, so the code can download the BERT model. Use -visible_gpus -1, after downloading, you could kill the process and rerun the code with multi-GPUs.

Train

python train.py  -ext_dropout 0.1 -lr 2e-3  -visible_gpus 1,2,3 -report_every 200 -save_checkpoint_steps 1000 -batch_size 1 -train_steps 100000 -accum_count 2  -log_file ../logs/ext_bert -use_interval true -warmup_steps 10000

To continue training from a checkpoint

python train.py  -ext_dropout 0.1 -lr 2e-3  -train_from ../models/model_step_99000.pt -visible_gpus 1,2,3 -report_every 200 -save_checkpoint_steps 1000 -batch_size 1 -train_steps 100000 -accum_count 2  -log_file ../logs/ext_bert -use_interval true -warmup_steps 10000

Test

python train.py -mode test  -test_batch_size 1 -bert_data_path ../bert_data -log_file ../logs/ext_bert_test -test_from ../models/model_step_99000.pt -model_path ../models -sep_optim true -use_interval true -visible_gpus 1,2,3 -alpha 0.95 -result_path ../results/ext
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Comments
  • TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

    TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first. 

    Traceback (most recent call last):
  File "train.py", line 108, in <module>
    train_ext(args, device_id)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/train_extractive.py", line 64, in train_ext
    train_single_ext(args, device_id)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/train_extractive.py", line 149, in train_single_ext
    trainer.train(train_iter_fct, args.train_steps)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/trainer_ext.py", line 138, in train
    self._gradient_accumulation( # this is the main function that calculates the loss
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/trainer_ext.py", line 318, in _gradient_accumulation
    sent_scores, mask = self.model(src, sections, token_sections, segs, clss, mask, mask_cls)
  File "/home/shangjl/anaconda3/envs/scibert/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/model_builder.py", line 135, in forward
    sent_scores = self.ext_layer(inputs_embeds, sections, attention_mask, extended_attention_mask).squeeze(-1)
  File "/home/shangjl/anaconda3/envs/scibert/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/longExtractiveFormer.py", line 148, in forward
    x = self.transformer_inter[i](i, x,
  File "/home/shangjl/anaconda3/envs/scibert/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/longExtractiveFormer.py", line 108, in forward
    output = self.self_attn(input_norm,
  File "/home/shangjl/anaconda3/envs/scibert/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/longExtractiveFormerAttention.py", line 718, in forward
    self_outputs = self.self(
  File "/home/shangjl/anaconda3/envs/scibert/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/longExtractiveFormerAttention.py", line 238, in forward
    attn_output = self._compute_attn_output_with_global_indices(
  File "/home/shangjl/CAIL/SciBERTSUM.git/trunk/src/models/longExtractiveFormerAttention.py", line 545, in _compute_attn_output_with_global_indices
    value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero].detach().numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

    value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero].detach().cpu().numpy() still didnt work

    opened by SJLMax 1
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