Multilingual-MiniLM-L12-H384

Name: Multilingual-MiniLM-L12-H384
Author: microsoft

72.0K

512

Small context

117M

17 languages

license:mit

microsoft

Other

OTHER

Fair

72K downloads

Community-tested

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Edge AI:

Mobile

Laptop

Server

1GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

run fine-tuning on XNLIbash

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

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