Brrrr

data_process.py

@@ -81,25 +81,25 @@ class QnA:
 def create_qna(row):
     # get rows; create uuid based on texts
     text = row['Text']
+    text_length= len(text)
     translation = row['Translated text']
     lang_from = converter.convert_code(row['Original lang'])
     lang_to = converter.convert_code(row['Target lang'])
     uuid_val = uuid.uuid3(uuid.NAMESPACE_OID, str(text + translation))
-    # json with language, uuid and langs-pair
-    METADATA = {"language": f"{lang_to}", "uuid": f"{uuid_val}", "langs-pair": f"{lang_from}-{lang_to}"}
+    # json with language, original text length, uuid and langs-pair
+    METADATA = {"language": f"{lang_to}", "length": f"{text_length}", "uuid": f"{uuid_val}", "langs-pair": f"{lang_from}-{lang_to}"}
     metadata_str = json.dumps(METADATA)
-    SOURCE = "tatoeba"
+    source = "tatoeba"
     # randomizing INSTRUCTION
-    INSTRUCTION = converter.RandomText.randomize_text(text, lang_to, lang_from)
-    RESPONSE = translation
-    return QnA(INSTRUCTION, RESPONSE, SOURCE, metadata_str)
+    instruction = converter.RandomText.randomize_text(text, lang_to, lang_from)
+    response = translation
+    return QnA(instruction, response, source, metadata_str)
 
 # load the dataset from Hugging Face
 hf_dataset = datasets.load_dataset('0x22almostEvil/tatoeba-mt-llama-only', split='train')
 
-# original is ~3M; with num_shards=55 it'll be ~65K
-hf_dataset = hf_dataset.shard(num_shards=55, index=0)
-
+# original is ~3M; with num_shards=30 it'll be ~120K
+hf_dataset = hf_dataset.shard(num_shards=30, index=0)
 print(hf_dataset)
 
 # convert the dataset to a pandas dataframe
@@ -110,4 +110,4 @@ qna_list = df.apply(create_qna, axis=1).tolist()
 
 # save the QnA objects as a parquet file
 qna_df = pd.DataFrame(qna_list, columns=["INSTRUCTION", "RESPONSE", "SOURCE", "METADATA"])
-qna_df.to_parquet("translation-taboeba-qna-65k-oa.parquet", row_group_size=100, engine="pyarrow", index=False)
+qna_df.to_parquet("translation-taboeba-qna-120k-oa.parquet", row_group_size=100, engine="pyarrow", index=False)