Update README.md

README.md

@@ -1,3 +1,159 @@
 ---
 license: apache-2.0
+pipeline_tag: fill-mask
+tags:
+- fill-mask
+- transformers
+- en
+- ko
+widget:
+  - text: 대한민국의 수도는 [MASK] 입니다.
 ---
+# mdistilbertV2.1
+
+- distilbert-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: 159,552개**(기존 bert 모델 vocab(119,548개)에 40,004개 (한글단어30,000개+영문10,000개+수동 4개)vocab 추가
+
+## Usage (HuggingFace Transformers)
+
+### 1. MASK 예시
+```python
+from transformers import AutoTokenizer, AutoModel, DistilBertForMaskedLM
+import torch
+import torch.nn.functional as F
+
+tokenizer = AutoTokenizer.from_pretrained('bongsoo/mdistilbertV3.1', do_lower_case=False)
+model = DistilBertForMaskedLM.from_pretrained('bongsoo/mdistilbertV3.1')
+
+text = ['한국의 수도는 [MASK] 이다', '에펠탑은 [MASK]에 있다', '충무공 이순신은 [MASK]에 최고의 장수였다']
+tokenized_input = tokenizer(text, max_length=128, truncation=True, padding='max_length', return_tensors='pt')
+
+outputs = model(**tokenized_input)
+logits = outputs.logits
+
+mask_idx_list = []
+for tokens in tokenized_input['input_ids'].tolist():
+    token_str = [tokenizer.convert_ids_to_tokens(s) for s in tokens]
+    
+    # **위 token_str리스트에서 [MASK] 인덱스를 구함
+    # => **해당 [MASK] 안덱스 값 mask_idx 에서는 아래 출력하는데 사용됨
+    mask_idx = token_str.index('[MASK]')
+    mask_idx_list.append(mask_idx)
+    
+for idx, mask_idx in enumerate(mask_idx_list):
+    
+    logits_pred=torch.argmax(F.softmax(logits[idx]), dim=1)
+    mask_logits_idx = int(logits_pred[mask_idx])
+    # [MASK]에 해당하는 token 구함
+    mask_logits_token = tokenizer.convert_ids_to_tokens(mask_logits_idx)
+    # 결과 출력 
+    print('\n')
+    print('*Input: {}'.format(text[idx]))
+    print('*[MASK] : {} ({})'.format(mask_logits_token, mask_logits_idx))
+```
+- 결과
+```
+*Input: 한국의 수도는 [MASK] 이다
+*[MASK] : 서울 (48253)
+
+
+*Input: 에펠탑은 [MASK]에 있다
+*[MASK] : 프랑스 (47364)
+
+
+*Input: 충무공 이순신은 [MASK]에 최고의 장수였다
+*[MASK] : 임진왜란 (121990)
+```
+### 2. 임베딩 예시
+- 평균 폴링(mean_pooling) 방식 사용. ([cls 폴링](https://huggingface.co/sentence-transformers/bert-base-nli-cls-token), [max 폴링](https://huggingface.co/sentence-transformers/bert-base-nli-max-tokens))
+
+```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']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('bongsoo/mdistilbertV3.1')
+model = AutoModel.from_pretrained('bongsoo/mdistilbertV3.1')
+
+# 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]}')
+```
+- 결과
+```
+Sentence embeddings:
+tensor([[-0.1137,  0.1491,  0.6711,  ..., -0.0217,  0.1839, -0.6143],
+        [ 0.0482, -0.0649,  0.5333,  ...,  0.1424, -0.0982, -0.3414]])
+*cosine_score:0.4784715175628662
+```
+## Training
+
+**MLM(Masked Langeuage Model) 훈련**
+- 입력 모델 : distilbert-base-multilingual-cased
+- 말뭉치 : 훈련 : bongsoo/moco-corpus-kowiki2022(7.6M) , 평가: ** bongsoo/moco_eva **
+- HyperParameter : **LearningRate : 5e-5, ** epochs: 12 **, batchsize: 32, max_token_len : 128**
+- vocab : **159,552개** (기존 bert 모델 vocab(119,548개)에 40,004개 (한글단어30,000개+영문10,000개+수동 4개)vocab 추가
+- 출력 모델 : mdistilbertV3.1 (size: 634MB)
+- 훈련시간 : 90h/1GPU (24GB/16.5 use)
+- loss : **훈련loss: 2.1154, 평가loss: 2.5275 **
+- 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/distilbert/distilbert-MLM-Trainer-V1.2.ipynb) 참조
+<br>perplexity 평가 코드는 [여기](https://github.com/kobongsoo/BERT/blob/master/distilbert/distilbert-perplexity-eval-V1.2.ipynb) 참조
+
+## Model Config
+```
+{
+  "_name_or_path": "",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.2",
+  "vocab_size": 159552
+}
+```
+## Citing & Authors
+
+bongsoo