Update README.md

README.md

@@ -27,24 +27,43 @@ target_lang="tr"  # change to your target lang
 
 from datasets import load_dataset
 #ted-multi is a multiple language translated dataset
-#fits for our case , not to big and curated
+#fits for our case , not to big and curated but need a simple processing
 
 dataset = load_dataset("ted_multi")
-
-#there is no Turkish lanugage in europarl, so will need to choose one
 dataset.cleanup_cache_files()
 
-
+#original from patrick's
 #chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]'  # change to the ignored characters of your fine-tuned model
 
 #will use cahya/wav2vec2-base-turkish-artificial-cv
 #checking inside model repository to find which chars removed (no run.sh)
 chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
 
+
+import re
+
+def extract_target_lang_entries(batch):
+  #specific mapping for ted_multi dataset
+  #need to find index of language in each translation as it can shift
+  try:
+    target_index_for_lang= batch["translations"]["language"].index(target_lang) 
+  except ValueError:
+    #target not in list empty it for later processing
+    batch["text"] = None 
+    return batch 
+
+  #index_translation_pairs = zip(batch, target_index_for_batch)
+  text= batch["translations"]["translation"][target_index_for_lang] 
+  batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
+  return batch
+
+
+#this dataset has additional columns need to say it
 cols_to_remove =  ['translations', 'talk_name']
 dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove)
 
 
+#on preocessing we tagged None for empty ones
 dataset_cleaned = dataset.filter(lambda x: x['text'] is not None)
 dataset_cleaned