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GLUE
zhezhaoa edited this page Sep 27, 2022
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Here is a short summary of our solution on GLUE classification benchmark. This section mainly focuses on single model.
The example of fine-tuning and doing inference on CoLA dataset with BERT-Base-uncased:
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/CoLA/train.tsv \
--dev_path datasets/CoLA/dev.tsv \
--output_model_path models/cola_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/cola_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/CoLA/test_nolabel.tsv \
--prediction_path datasets/CoLA/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on CoLA dataset with RoBERTa-Base: Since RoBERTa-Base uses different special tokens, it is necessary to change the path of special token mapping from models/special_tokens_map.json to models/xlmroberta_special_tokens_map.json in tencentpretrain/utils/constants.py
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/CoLA/train.tsv \
--dev_path datasets/CoLA/dev.tsv \
--output_model_path models/cola_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/cola_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/CoLA/test_nolabel.tsv \
--prediction_path datasets/CoLA/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on SST-2 dataset with BERT-Base-uncased:
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/SST-2/train.tsv \
--dev_path datasets/SST-2/dev.tsv \
--output_model_path models/sst-2_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/sst-2_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/SST-2/test_nolabel.tsv \
--prediction_path datasets/SST-2/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on SST-2 dataset with RoBERTa-Base:
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/SST-2/train.tsv \
--dev_path datasets/SST-2/dev.tsv \
--output_model_path models/sst-2_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/sst-2_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/SST-2/test_nolabel.tsv \
--prediction_path datasets/SST-2/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on QQP dataset with BERT-Base-uncased:
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/QQP/train.tsv \
--dev_path datasets/QQP/dev.tsv \
--output_model_path models/qqp_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/qqp_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/QQP/test_nolabel.tsv \
--prediction_path datasets/QQP/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on QQP dataset with RoBERTa-Base:
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/QQP/train.tsv \
--dev_path datasets/QQP/dev.tsv \
--output_model_path models/qqp_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/qqp_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/QQP/test_nolabel.tsv \
--prediction_path datasets/QQP/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on QNLI dataset with BERT-Base-uncased:
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/QNLI/train.tsv \
--dev_path datasets/QNLI/dev.tsv \
--output_model_path models/qnli_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/qnli_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/QNLI/test_nolabel.tsv \
--prediction_path datasets/QNLI/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on QNLI dataset with RoBERTa-Base:
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/QNLI/train.tsv \
--dev_path datasets/QNLI/dev.tsv \
--output_model_path models/qnli_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/qnli_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/QNLI/test_nolabel.tsv \
--prediction_path datasets/QNLI/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on WNLI dataset with BERT-Base-uncased:
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/WNLI/train.tsv \
--dev_path datasets/WNLI/dev.tsv \
--output_model_path models/wnli_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/wnli_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/WNLI/test_nolabel.tsv \
--prediction_path datasets/WNLI/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on WNLI dataset with RoBERTa-Base:
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/WNLI/train.tsv \
--dev_path datasets/WNLI/dev.tsv \
--output_model_path models/wnli_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/wnli_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/WNLI/test_nolabel.tsv \
--prediction_path datasets/WNLI/prediction.tsv \
--seq_length 128 --labels_num 2
MNLI dataset consists of two development sets and two test sets. They are respectively named MNLI-m and MNLI-mm. The example of fine-tuning and doing inference on MNLI-m and MNLI-mm with BERT-Base-uncased:
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/MNLI/train.tsv \
--dev_path datasets/MNLI/dev_m.tsv \
--output_model_path models/mnli-m_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/mnli-m_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/MNLI/test_nolabel_m.tsv \
--prediction_path datasets/MNLI/prediction_m.tsv \
--seq_length 128 --labels_num 3
python3 finetune/run_classifier.py --pretrained_model_path models/bert_base_uncased.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/MNLI/train.tsv \
--dev_path datasets/MNLI/dev_mm.tsv \
--output_model_path models/mnli_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/mnli-mm_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/MNLI/test_nolabel_mm.tsv \
--prediction_path datasets/MNLI/prediction_mm.tsv \
--seq_length 128 --labels_num 3
The example of fine-tuning and doing inference on MNLI-m and MNLI-mm with RoBERTa-Base:
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/MNLI/train.tsv \
--dev_path datasets/MNLI/dev_m.tsv \
--output_model_path models/mnli-m_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/mnli-m_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/MNLI/test_nolabel_m.tsv \
--prediction_path datasets/MNLI/prediction_m.tsv \
--seq_length 128 --labels_num 3
python3 finetune/run_classifier.py --pretrained_model_path models/roberta_base.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/MNLI/train.tsv \
--dev_path datasets/MNLI/dev_mm.tsv \
--output_model_path models/mnli_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/mnli-mm_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/MNLI/test_nolabel_mm.tsv \
--prediction_path datasets/MNLI/prediction_mm.tsv \
--seq_length 128 --labels_num 3
It is pointed out in RoBERTa that better results can be achieved for MRPC, RTE, and STS-B by fine-tuning upon MNLI-m model. We follow this setting. Since MRPC, RTE, STS-B have different targets with MNLI, we remove the target layer of MNLI model before using it.
import torch
input_model = torch.load("models/mnli-m_classifier_model.bin", map_location="cpu")
for key in list(input_model.keys()):
if "output_layer_2" in key:
del input_model[key]
torch.save(input_model, "models/mnli-m_classifier_del_model.bin")
The example of fine-tuning and doing inference on MRPC dataset with BERT-Base-uncased fine-tuned on MNLI-m:
python3 finetune/run_classifier.py --pretrained_model_path models/mnli-m_classifier_del_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/MRPC/train.tsv \
--dev_path datasets/MRPC/dev.tsv \
--output_model_path models/mrpc_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/mrpc_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/MRPC/test_nolabel.tsv \
--prediction_path datasets/MRPC/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on MRPC dataset with RoBERTa-Base fine-tuned on MNLI-m:
python3 finetune/run_classifier.py --pretrained_model_path models/mnli-m_classifier_del_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/MRPC/train.tsv \
--dev_path datasets/MRPC/dev.tsv \
--output_model_path models/mrpc_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/mrpc_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/MRPC/test_nolabel.tsv \
--prediction_path datasets/MRPC/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on RTE dataset with BERT-Base-uncased fine-tuned on MNLI-m:
python3 finetune/run_classifier.py --pretrained_model_path models/mnli-m_classifier_del_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/RTE/train.tsv \
--dev_path datasets/RTE/dev.tsv \
--output_model_path models/rte_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/rte_classifier_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/RTE/test_nolabel.tsv \
--prediction_path datasets/RTE/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on RTE dataset with RoBERTa-Base fine-tuned on MNLI-m:
python3 finetune/run_classifier.py --pretrained_model_path models/mnli-m_classifier_del_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/RTE/train.tsv \
--dev_path datasets/RTE/dev.tsv \
--output_model_path models/rte_classifier_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_classifier_infer.py --load_model_path models/rte_classifier_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/RTE/test_nolabel.tsv \
--prediction_path datasets/RTE/prediction.tsv \
--seq_length 128 --labels_num 2
The example of fine-tuning and doing inference on STS-B dataset with BERT-Base-uncased fine-tuned on MNLI-m:
python3 finetune/run_regression.py --pretrained_model_path models/mnli-m_classifier_del_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--train_path datasets/STS-B/train.tsv \
--dev_path datasets/STS-B/dev.tsv \
--output_model_path models/sts-b_regression_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_regression_infer.py --load_model_path models/sts-b_regression_model.bin \
--vocab_path models/google_uncased_en_vocab.txt \
--config_path models/bert/base_config.json \
--test_path datasets/STS-B/test_nolabel.tsv \
--prediction_path datasets/STS-B/prediction.tsv \
--seq_length 128
The example of fine-tuning and doing inference on STS-B dataset with RoBERTa-Base fine-tuned on MNLI-m:
python3 finetune/run_regression.py --pretrained_model_path models/mnli-m_classifier_del_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--train_path datasets/STS-B/train.tsv \
--dev_path datasets/STS-B/dev.tsv \
--output_model_path models/sts-b_regression_model.bin \
--epochs_num 3 --batch_size 32
python3 inference/run_regression_infer.py --load_model_path models/sts-b_regression_model.bin \
--vocab_path models/huggingface_gpt2_vocab.txt \
--merges_path models/huggingface_gpt2_merges.txt \
--tokenizer bpe \
--config_path models/xlm-roberta/base_config.json \
--test_path datasets/STS-B/test_nolabel.tsv \
--prediction_path datasets/STS-B/prediction.tsv \
--seq_length 128