init

training_scripts/finetune_t5.py

@@ -221,6 +221,7 @@ def train(
             set([_l for __i, _l in zip(_i[dataset_column_label], label_list) if __i == 1]) for _i in dataset_tmp
         ]
         eval_metric = get_f1_score(_references, output)
+        eval_metric[f'f1/{dataset}/{dataset_name}'] = eval_metric.pop('f1')
         logging.info(json.dumps(eval_metric, indent=4))
         with open(f'{output_dir}/best_model/evaluation_metrics.json', 'w') as f:
             json.dump(eval_metric, f)

training_scripts/script.sh

@@ -1,6 +1,13 @@
 
-# TweetTopic
+# English TweetTopic
 python finetune_t5.py --dataset-name en_2022 --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en-2022 --model-organization cardiffnlp --use-auth-token
+# Zero-shot
+python finetune_t5.py -m cardiffnlp/mt5-small-tweet-topic-multi-en-2022 --dataset-name en --skip-train --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en-2022 --model-organization cardiffnlp --use-auth-token
+python finetune_t5.py -m cardiffnlp/mt5-small-tweet-topic-multi-en-2022 --dataset-name en --skip-train --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en-2022 --model-organization cardiffnlp --use-auth-token
+python finetune_t5.py -m cardiffnlp/mt5-small-tweet-topic-multi-en-2022 --dataset-name en --skip-train --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en-2022 --model-organization cardiffnlp --use-auth-token
+python finetune_t5.py -m cardiffnlp/mt5-small-tweet-topic-multi-en-2022 --dataset-name en --skip-train --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en-2022 --model-organization cardiffnlp --use-auth-token
+
+
 
 python finetune_t5.py --dataset-name es --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-es --model-organization cardiffnlp --use-auth-token
 python finetune_t5.py --dataset-name en --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-multi-en --model-organization cardiffnlp --use-auth-token