Update README.md

README.md

@@ -6,51 +6,21 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+license: cc-by-sa-4.0
+datasets:
+- klue
+language:
+- ko
 ---
 
-# {MODEL_NAME}
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
+본 모델은 multi-task loss (MultipleNegativeLoss -> AnglELoss) 로 학습되었습니다.
 
 ## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
 # Sentences we want sentence embeddings for
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
@@ -58,87 +28,41 @@ sentences = ['This is an example sentence', 'Each sentence is converted']
 tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
 model = AutoModel.from_pretrained('{MODEL_NAME}')
 
-# Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
-# Compute token embeddings
+# I used mean-pool method for sentence representation
 with torch.no_grad():
     model_output = model(**encoded_input)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    representations, _ = self.model(**inputs, return_dict=False)
+    attention_mask = inputs["attention_mask"]
+    input_mask_expanded = (attention_mask.unsqueeze(-1).expand(representations.size()).to(representations.dtype))
+    summed = torch.sum(representations * input_mask_expanded, 1)
+    sum_mask = input_mask_expanded.sum(1)
+    sum_mask = torch.clamp(sum_mask, min=1e-9)
+    res.extend(summed.cpu() / sum_mask.cpu()) # mean_pool
 
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
 ```
 
 
-
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+| Organization | Backbone Model | KlueSTS average | KorSTS average |
+| -------- | ------- | ------- | ------- |
+| team-lucid | DeBERTa-base | 54.15 | 29.72 |
+| monologg | Electra-base | 66.97 | 29.72 |
+| LMkor | Electra-base | 70.98 | 43.09 |
+| deliciouscat | DeBERTa-base | - | 67.65 |
+| BM-K    | Roberta-base | 82.93 | **85.77** |
+| Klue    | Roberta-large | **86.71** | 71.70 |
+| Klue (Hyperparameter searched) | Roberta-large | 86.21 | 75.54 |
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+기존 한국어 문장 임베딩 모델은 mnli, snli 등 영어 데이터셋을 기계번역하여 학습된 점을 참고삼아 Klue 데이터셋으로 대신 학습해 보았습니다.
 
+그 결과, Klue-Roberta-large 모델 기반으로 학습했을 경우 KlueSTS 및 KorSTS 테스트셋에 모두에 대해 준수한 성능을 보여, 좀 더 elaborate한 representation을 형성하는 것으로 사료했습니다.
 
-## Training
-The model was trained with the parameters:
+다만 평가 수치는 하이퍼파라미터 세팅, 시드 넘버 등으로 크게 달라질 수 있으므로 참고하시길 바랍니다.
 
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 503 with parameters:
-```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 730 with parameters:
-```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.AnglELoss.AnglELoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'pairwise_angle_sim'}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 5,
-    "evaluation_steps": 0,
-    "evaluator": "CustomizedESEv.customizedEmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 50,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: RobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## Citing & Authors
+## Training
+NegativeRank loss -> simcse loss 로 학습했습니다.
 
-<!--- Describe where people can find more information -->