Update: 최종 완료 모델에 대한 README 확정

README.md

@@ -20,25 +20,25 @@ model-index:
       name: text2text-generation             # Optional. Example: Speech Recognition
     metrics:
       - type: bleu         # Required. Example: wer. Use metric id from https://hf.co/metrics
-        value: 0.9161441917016176       # Required. Example: 20.90
+        value: 0.9313276940897475       # Required. Example: 20.90
         name: eval_bleu         # Optional. Example: Test WER
-        verified: true              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
+        verified: false              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
       - type: rouge1         # Required. Example: wer. Use metric id from https://hf.co/metrics
-        value: 0.9502159661745533       # Required. Example: 20.90
+        value: 0.9607081256861959       # Required. Example: 20.90
         name: eval_rouge1         # Optional. Example: Test WER
-        verified: true              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
+        verified: false             # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
       - type: rouge2         # Required. Example: wer. Use metric id from https://hf.co/metrics
-        value: 0.9313935147887745       # Required. Example: 20.90
+        value: 0.9394649136169404       # Required. Example: 20.90
         name: eval_rouge2       # Optional. Example: Test WER
-        verified: true              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
+        verified: false              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
       - type: rougeL         # Required. Example: wer. Use metric id from https://hf.co/metrics
-        value: 0.950015374196916       # Required. Example: 20.90
+        value: 0.9605735834651536       # Required. Example: 20.90
         name: eval_rougeL        # Optional. Example: Test WER
-        verified: true              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
+        verified: false              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
       - type: rougeLsum         # Required. Example: wer. Use metric id from https://hf.co/metrics
-        value: 0.9500390902948073       # Required. Example: 20.90
+        value: 0.9605993760190767       # Required. Example: 20.90
         name: eval_rougeLsum        # Optional. Example: Test WER
-        verified: true              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
+        verified: false              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
 ---
 
 # ko-barTNumText(TNT Model🧨): Try Number To Korean Reading(숫자를 한글로 바꾸는 모델)
@@ -78,33 +78,12 @@ aihub에서 데이터를 받으실 분은 한국인일 것이므로, 한글로
   
   
 ## Uses
-This Model is inferenced token BACKWARD. so, you have to `flip` before `tokenizer.decode()` <br />
-해당 모델은 inference시 역순으로 예측합니다. (밥을 6시에 먹었어 -> 어 먹었 시에 여섯 을 밥) <br />
-때문에 `tokenizer.decode`를 수행하기 전에, `flip`으로 역순으로 치환해주세요.
-
 Want see more detail follow this URL [KoGPT_num_converter](https://github.com/ddobokki/KoGPT_num_converter) <br /> and see `bart_inference.py` and `bart_train.py`
-```python
-class BartText2TextGenerationPipeline(Text2TextGenerationPipeline):
-    def postprocess(self, model_outputs, return_type=ReturnType.TEXT, clean_up_tokenization_spaces=False):
-        records = []
-        reversed_model_outputs = torch.flip(model_outputs["output_ids"][0], dims=[-1])
-        for output_ids in reversed_model_outputs:
-            if return_type == ReturnType.TENSORS:
-                record = {f"{self.return_name}_token_ids": output_ids}
-            elif return_type == ReturnType.TEXT:
-                record = {
-                    f"{self.return_name}_text": self.tokenizer.decode(
-                        output_ids,
-                        skip_special_tokens=True,
-                        clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-                    )
-                }
-            records.append(record)
-        return records
-```
+
 ## Evaluation
 Just using `evaluate-metric/bleu` and `evaluate-metric/rouge` in huggingface `evaluate` library <br />
-[Training wanDB URL](https://wandb.ai/bart_tadev/BartForConditionalGeneration/runs/2dt1d2b0?workspace=user-bart_tadev)
+[Training wanDB URL](https://wandb.ai/bart_tadev/BartForConditionalGeneration/runs/326xgytt?workspace=user-bart_tadev)
+
 ## How to Get Started With the Model
 ```python
 from transformers.pipelines import Text2TextGenerationPipeline
@@ -112,8 +91,7 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 texts = ["그러게 누가 6시까지 술을 마시래?"]
 tokenizer = AutoTokenizer.from_pretrained("lIlBrother/ko-barTNumText")
 model = AutoModelForSeq2SeqLM.from_pretrained("lIlBrother/ko-barTNumText")
-# BartText2TextGenerationPipeline is implemented above (see 'Use')
-seq2seqlm_pipeline = BartText2TextGenerationPipeline(model=model, tokenizer=tokenizer)
+seq2seqlm_pipeline = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
 kwargs = {
     "min_length": 0,
     "max_length": 1206,