Fix eval ref miss in Chinese WWM.

examples/language-modeling/chinese_ref.py → examples/contrib/run_chinese_ref.py

@@ -118,7 +118,7 @@ def main(args):
     # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
     with open(args.file_name, "r", encoding="utf-8") as f:
         data = f.readlines()
-
+    data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
     ltp_tokenizer = LTP(args.ltp)  # faster in GPU device
     bert_tokenizer = BertTokenizer.from_pretrained(args.bert)
 

examples/language-modeling/README.md

@@ -63,7 +63,7 @@ python run_language_modeling.py \
     --whole_word_mask
 ```
 
-For Chinese models, it's same with English model with only --mlm`. If using whole-word masking, we need to generate a reference files, case it's char level.
+For Chinese models, it's same with English model with only `--mlm`. If using whole-word masking, we need to generate a reference files, because it's char level.
 
 **Q :** Why ref file ?
 
@@ -76,15 +76,19 @@ So we need a ref file to tell model which pos of BERT original token should be a
 **A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT. It works well on so many Chines Task like CLUE (Chinese GLUE).
 They use LTP, so if we want to fine-tune their model, we need LTP.
 
+Now LTP only only works well on `transformers==3.2.0`. So we don't add it to requirements.txt.
+You need to check to `3.2.0` for `run_chinese_ref.py`. And the code  could be found in `examples/contrib`.
+
+
 ```bash
 export TRAIN_FILE=/path/to/dataset/wiki.train.raw
 export LTP_RESOURCE=/path/to/ltp/tokenizer
 export BERT_RESOURCE=/path/to/bert/tokenizer
 export SAVE_PATH=/path/to/data/ref.txt
 
-python chinese_ref.py \
+python examples/contrib/run_chinese_ref.py \
     --file_name=$TRAIN_FILE \
-    --ltp=$LTP_RESOURCE
+    --ltp=$LTP_RESOURCE \
     --bert=$BERT_RESOURCE \
     --save_path=$SAVE_PATH 
 ```

examples/language-modeling/run_language_modeling.py

@@ -103,9 +103,13 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
     )
-    chinese_ref_file: Optional[str] = field(
+    train_ref_file: Optional[str] = field(
         default=None,
-        metadata={"help": "An optional input ref data file for whole word mask in Chinees."},
+        metadata={"help": "An optional input train ref data file for whole word mask in Chinese."},
+    )
+    eval_ref_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."},
     )
     line_by_line: bool = field(
         default=False,
@@ -148,16 +152,16 @@ def get_dataset(
     evaluate: bool = False,
     cache_dir: Optional[str] = None,
 ):
-    def _dataset(file_path):
+    def _dataset(file_path, ref_path=None):
         if args.line_by_line:
-            if args.chinese_ref_file is not None:
+            if ref_path is not None:
                 if not args.whole_word_mask or not args.mlm:
                     raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask")
                 return LineByLineWithRefDataset(
                     tokenizer=tokenizer,
                     file_path=file_path,
                     block_size=args.block_size,
-                    ref_path=args.chinese_ref_file,
+                    ref_path=ref_path,
                 )
 
             return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
@@ -171,11 +175,11 @@ def _dataset(file_path):
             )
 
     if evaluate:
-        return _dataset(args.eval_data_file)
+        return _dataset(args.eval_data_file, args.eval_ref_file)
     elif args.train_data_files:
         return ConcatDataset([_dataset(f) for f in glob(args.train_data_files)])
     else:
-        return _dataset(args.train_data_file)
+        return _dataset(args.train_data_file, args.train_ref_file)
 
 
 def main():

src/transformers/data/datasets/language_modeling.py

@@ -128,15 +128,17 @@ def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: i
         logger.info("Creating features from dataset file at %s", file_path)
         logger.info("Use ref segment results at %s", ref_path)
         with open(file_path, encoding="utf-8") as f:
-            data = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
-        batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size)
-        self.examples = batch_encoding["input_ids"]
-        self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]
-
+            data = f.readlines()  # use this method to avoid delimiter '\u2029' to split a line
+        data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]
         # Get ref inf from file
         with open(ref_path, encoding="utf-8") as f:
             ref = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
         assert len(data) == len(ref)
+
+        batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size)
+        self.examples = batch_encoding["input_ids"]
+        self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]
+
         n = len(self.examples)
         for i in range(n):
             self.examples[i]["chinese_ref"] = torch.tensor(ref[i], dtype=torch.long)