init

experiments/main.sh

@@ -4,10 +4,9 @@ MODEL="cardiffnlp/twitter-roberta-base-2021-124m"
 MODEL="roberta-base"
 # topic, ner, nerd[hw]
 MODEL="cardiffnlp/twitter-roberta-base-2022-154m"
-
-# topic [hawk], ner, nerd[ukri]
+# topic, ner, nerd[ukri]
 MODEL="jhu-clsp/bernice"
-# topic, ner [hawk]
+# topic, ner [hawk], nerd[hw]
 MODEL="vinai/bertweet-base"
 
 
@@ -36,8 +35,10 @@ python model_finetuning_ner.py -m "${MODEL}" -d "ner_random0_seed1"
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random1_seed1"
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random2_seed1"
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random3_seed1"
+
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random0_seed2"
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random1_seed2"
+
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random2_seed2"
 python model_finetuning_ner.py -m "${MODEL}" -d "ner_random3_seed2"
 
@@ -49,14 +50,9 @@ python model_finetuning_topic.py -m "${MODEL}" -d "topic_random2_seed0"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random3_seed0"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random0_seed1"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random1_seed1"
-[ulri]
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random2_seed1"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random3_seed1"
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random0_seed2"
-[stone]
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random1_seed2"
-rm -rf ckpt
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random2_seed2"
-rm -rf ckpt
 python model_finetuning_topic.py -m "${MODEL}" -d "topic_random3_seed2"
-rm -rf ckpt

experiments/model_finetuning_ner.py

@@ -110,18 +110,18 @@ def main(
             max_length=128)
         all_labels = examples["gold_label_sequence"]
         new_labels = []
-        if tokenizer.is_fast:
-            for ind, labels in enumerate(all_labels):
-                word_ids = tokenized_inputs.word_ids(ind)
-                new_labels.append(align_labels_with_tokens(labels, word_ids))
-        else:
-            for token, label in zip(tokens, all_labels):
-                tmp_labels = [-100]
-                for to, la in zip(token, label):
-                    to_tokenized = tokenizer.tokenize(to)
-                    tmp_labels += [la] * len(to_tokenized)
-                tmp_labels = tmp_labels + [-100] * (128 - len(tmp_labels))
-                new_labels.append(tmp_labels)
+        # if tokenizer.is_fast:
+        #     for ind, labels in enumerate(all_labels):
+        #         word_ids = tokenized_inputs.word_ids(ind)
+        #         new_labels.append(align_labels_with_tokens(labels, word_ids))
+        # else:
+        for token, label in zip(tokens, all_labels):
+            tmp_labels = [-100]
+            for to, la in zip(token, label):
+                to_tokenized = tokenizer.tokenize(to)
+                tmp_labels += [la] * len(to_tokenized)
+            tmp_labels = tmp_labels + [-100] * (128 - len(tmp_labels))
+            new_labels.append(tmp_labels)
         tokenized_inputs["labels"] = new_labels
         return tokenized_inputs