add key2gen+noisy w/o rg script

convlab/base_models/gpt/keyword_extraction/train_t5_key2gen+key2gen_noisy.sh

+set -e
+n_gpus=8
+master_port=23456
+task_name="key2gen+key2gen_noisy"
+dataset_name="dailydialog+metalwoz+tm1+tm2+tm3+sgd+reddit+wikidialog"
+model_type="gpt"
+model_name="t5-small"
+data_dir="data/${task_name}/${model_type}/${dataset_name}"
+output_dir="output/${task_name}/${model_name}/${dataset_name}"
+cache_dir="../cache"
+logging_dir="${output_dir}/runs"
+train_file="${data_dir}/train.json"
+source_column="source"
+target_column="target"
+truncation_side="left"
+max_source_length=512
+max_target_length=128
+model_name_or_path="${model_name}"
+per_device_train_batch_size=64
+per_device_eval_batch_size=128
+gradient_accumulation_steps=1
+num_workers=16
+lr=1e-3
+num_train_epochs=1
+
+python -m torch.distributed.launch --master_port ${master_port} \
+    --nproc_per_node ${n_gpus} ../../t5/run_seq2seq.py \
+    --task_name ${task_name} \
+    --train_file ${train_file} \
+    --source_column ${source_column} \
+    --target_column ${target_column} \
+    --max_source_length ${max_source_length} \
+    --max_target_length ${max_target_length} \
+    --truncation_side ${truncation_side} \
+    --model_name_or_path ${model_name_or_path} \
+    --do_train \
+    --save_steps 5000 \
+    --save_total_limit 3 \
+    --cache_dir ${cache_dir} \
+    --output_dir ${output_dir} \
+    --logging_dir ${logging_dir} \
+    --preprocessing_num_workers ${num_workers} \
+    --dataloader_num_workers ${num_workers} \
+    --per_device_train_batch_size ${per_device_train_batch_size} \
+    --per_device_eval_batch_size ${per_device_eval_batch_size} \
+    --gradient_accumulation_steps ${gradient_accumulation_steps} \
+    --learning_rate ${lr} \
+    --num_train_epochs ${num_train_epochs} \
+    --optim adafactor \
+    --lr_scheduler_type constant \
+    --gradient_checkpointing

convlab/base_models/t5/key2gen/evaluate.py

@@ -67,12 +67,12 @@ if __name__ == '__main__':
                         filename = os.path.join(output_dir, task_name, f"{dataset_name}_{shot}shot_order{dial_ids_order}/gen{test_split}/generated_predictions.json")
                         results.append(evaluate(filename, metric))
                     res = {
-                        "dataset": f"{task_name}-{shot}shot",
-                        "model": f"{model_name}{test_split}",
+                        "dataset": f"{task_name}{test_split}-{shot}shot",
+                        "model": f"{model_name}",
                         **avg_result(results)
                     }
                     tables.append(res)
                     # print(res)
     res = tabulate(tables, headers='keys', tablefmt='github')
-    with open(f'eval_results.txt', 'w', encoding='utf-8') as f:
+    with open(f'eval_results.txt', 'a+', encoding='utf-8') as f:
         print(res, file=f)