Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# albert-small-kor-sbert-v1
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -27,7 +27,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('bongsoo/albert-small-kor-sbert-v1')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -50,8 +50,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('bongsoo/albert-small-kor-sbert-v1')
+model = AutoModel.from_pretrained('bongsoo/albert-small-kor-sbert-v1')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -71,53 +71,55 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+- 성능 측정을 위한 말뭉치는,  아래 한국어 (kor), 영어(en)  평가 말뭉치를 이용함
+<br> 한국어 : **korsts(1,379쌍문장)** 와 **klue-sts(519쌍문장)** 
+<br> 영어 : [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt)(1,376쌍문장) 와 [glue:stsb](https://huggingface.co/datasets/glue/viewer/stsb/validation) (1,500쌍문장)
+- 성능 지표는 **cosin.spearman**
+- 평가 측정 코드는 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-test3.ipynb) 참조
+- 
+|모델     |korsts|klue-sts|glue(stsb)|stsb_multi_mt(en)|
+|:--------|------:|--------:|--------------:|------------:|
+|distiluse-base-multilingual-cased-v2   |0.7475    |0.7855    |0.8193           |0.8075|
+|paraphrase-multilingual-mpnet-base-v2  |0.8201    |0.7993    |0.8907           |0.8682|
+|bongsoo/moco-sentencedistilbertV2.1    |0.8390    |0.8767    |0.8805           |0.8548|
+|bongsoo/albert-small-kor-sbert-v1      |0.8305    |0.8588    |0.8419           |0.7965|
 
 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
 
 ## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 1303 with parameters:
-```
-{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
+- sts(10)-distil(10)-nli(3)-sts(10)
 
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+The model was trained with the parameters:
 
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 10,
-    "evaluation_steps": 2605,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "eps": 1e-06,
-        "lr": 0.0001
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 1303,
-    "weight_decay": 0.01
-}
-```
+**공통**
+- **do_lower_case=1, correct_bios=0, polling_mode=cls**
+  
+**1.STS**
+- 말뭉치 : korsts(5,749) + kluestsV1.1(11,668) + stsb_multi_mt(5,749) + mteb/sickr-sts(9,927) + glue stsb(5,749) (총:38,842)
+- Param : **lr: 1e-4, eps: 1e-6, warm_step=10%, epochs: 10, train_batch: 32, eval_batch: 64, max_token_len: 72**
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentece-bert-sts.ipynb) 참조
+    
+**2.distilation**
+- 교사 모델 : paraphrase-multilingual-mpnet-base-v2(max_token_len:128)
+- 말뭉치 : news_talk_en_ko_train.tsv (영어-한국어 대화-뉴스 병렬 말뭉치 : 1.38M)
+- Param : **lr: 5e-5, epochs: 10, train_batch: 128, eval/test_batch: 64, max_token_len: 128(교사모델이 128이므로 맟춰줌)**
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-distillaton.ipynb) 참조
+
+**3.NLI**
+- 말뭉치 : 훈련(967,852) : kornli(550,152), kluenli(24,998), glue-mnli(392,702) / 평가(3,519) : korsts(1,500), kluests(519), gluests(1,500) ()
+- HyperParameter : **lr: 3e-5, eps: 1e-8, warm_step=10%, epochs: 3, train/eval_batch: 64, max_token_len: 128**
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentence-bert-nli.ipynb) 참조
 
 
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 72, 'do_lower_case': True}) with Transformer model: AlbertModel 
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': True}) with Transformer model: AlbertModel 
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
 ```
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+bongsoo