Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - en
 - ko
 ---
-# mdistilbertV2.0
+# mbertV2.0
 
 - bert-base-multilingual-cased 모델에 [moco-corpus-kowiki2022 말뭉치](https://huggingface.co/datasets/bongsoo/moco-corpus-kowiki2022)(kowiki202206 + MOCOMSYS 추출 3.2M 문장)로  vocab 추가하여 학습 시킨 모델
 - **vocab: 152,537개**(기존 bert 모델 vocab(119,548개)에 32,989개 vocab 추가)
@@ -18,10 +18,42 @@ tags:
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-tokenizer = AutoTokenizer.from_pretrained('bongsoo/mbertV2.0', do_lower_case=False)
+
+#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']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('bongsoo/mbertV2.0')
 model = AutoModel.from_pretrained('bongsoo/mbertV2.0')
 
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+
+# sklearn 을 이용하여 cosine_scores를 구함
+# => 입력값 embeddings 은 (1,768) 처럼 2D 여야 함.
+from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances
+cosine_scores = 1 - (paired_cosine_distances(sentence_embeddings[0].reshape(1,-1), sentence_embeddings[1].reshape(1,-1)))
+
+print(f'*cosine_score:{cosine_scores[0]}')
 ```
+
 ## Training
 
 **MLM(Masked Langeuage Model) 훈련**