Update: 리드미 완전 추가

README.md

@@ -16,8 +16,8 @@ model-index:
 - name: barTNumText
   results:
   - task:
-      type: translation             # Required. Example: automatic-speech-recognition
-      name: translation             # Optional. Example: Speech Recognition
+      type: text2text-generation             # Required. Example: automatic-speech-recognition
+      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
@@ -39,4 +39,88 @@ model-index:
         value: 0.9500390902948073       # 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).
----
+---
+
+# CamemBERT: a Tasty French Language Model
+
+## Table of Contents
+- [CamemBERT: a Tasty French Language Model](#camembert-a-tasty-french-language-model)
+  - [Table of Contents](#table-of-contents)
+  - [Model Details](#model-details)
+  - [Uses](#uses)
+  - [Evaluation](#evaluation)
+  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+## Model Details
+- **Model Description:**
+뭔가 찾아봐도 모델이나 알고리즘이 딱히 없어서 만들어본 모델입니다. <br />
+BartForConditionalGeneration Fine-Tuning Model For Number To Korean <br />
+BartForConditionalGeneration으로 파인튜닝한, 숫자를 한글로 변환하는 Task 입니다. <br />
+
+Dataset use [Korea aihub](https://aihub.or.kr/aihubdata/data/list.do?currMenu=115&topMenu=100&srchDataRealmCode=REALM002&srchDataTy=DATA004) <br />
+I can't open my fine-tuning datasets for my private issue <br />
+데이터셋은 Korea aihub에서 받아서 사용하였으며, 파인튜닝에 사용된 모든 데이터를 사정상 공개해드릴 수는 없습니다. <br />
+
+Korea aihub data is ONLY permit to Korean!!!!!!! <br />
+aihub에서 데이터를 받으실 분은 한국인일 것이므로, 한글로만 작성합니다. <br />
+정확히는 음성전사를 철자전사로 번역하는 형태로 학습된 모델입니다. (ETRI 전사기준) <br />
+
+In case, ten million, some people use 10 million or some people use 10000000, so this model is crucial for training datasets
+천만을 1000만 혹은 10000000으로 쓸 수도 있기에, Training Datasets에 따라 결과는 상이할 수 있습니다. <br />
+- **Developed by:**  Yoo SungHyun(https://github.com/YooSungHyun)
+- **Language(s):** Korean
+- **License:** apache-2.0
+- **Parent Model:** See the [kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) for more information about the pre-trained base model.
+  
+  
+## Uses
+This Model is inferenced token BACKWARD. so, you have to `flip` before `tokenizer.decode()`
+해당 모델은 inference시 역순으로 예측합니다. (밥을 6시에 먹었어 -> 어 먹었 시에 여섯 을 밥) 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) 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 
+## How to Get Started With the Model
+```python
+from transformers.pipelines import Text2TextGenerationPipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+texts = ["그러게 누가 6시까지 술을 마시래?"]
+tokenizer = AutoTokenizer.from_pretrained(
+    inference_args.model_name_or_path,
+)
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    inference_args.model_name_or_path,
+)
+# BartText2TextGenerationPipeline is implemented above (see 'Use')
+seq2seqlm_pipeline = BartText2TextGenerationPipeline(model=model, tokenizer=tokenizer)
+kwargs = {
+    "min_length": args.min_length,
+    "max_length": args.max_length,
+    "num_beams": args.beam_width,
+    "do_sample": args.do_sample,
+    "num_beam_groups": args.num_beam_groups,
+}
+pred = seq2seqlm_pipeline(texts, **kwargs)
+print(pred)
+# 그러게 누가 여섯 시까지 술을 마시래?
+```