File size: 4,782 Bytes
3d13862
 
 
 
 
 
869e0db
3d13862
 
44c00e0
3d13862
 
 
 
 
 
 
869e0db
3d13862
 
869e0db
3d13862
 
 
 
 
 
44c00e0
3d13862
44c00e0
3d13862
 
 
 
 
 
869e0db
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3d13862
869e0db
 
 
3d13862
869e0db
 
 
 
 
 
 
 
 
 
3d13862
44c00e0
 
869e0db
 
 
 
 
 
 
 
 
 
3d13862
 
 
 
 
 
869e0db
3d13862
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
869e0db
 
3d13862
 
 
 
 
 
 
 
869e0db
3d13862
 
 
 
 
 
 
 
869e0db
3d13862
 
 
 
 
 
44c00e0
869e0db
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---

# ko-sbert-multitask

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.

<!--- 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 = ["์•ˆ๋…•ํ•˜์„ธ์š”?", "ํ•œ๊ตญ์–ด ๋ฌธ์žฅ ์ž„๋ฒ ๋”ฉ์„ ์œ„ํ•œ ๋ฒ„ํŠธ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค."]

model = SentenceTransformer('jhgan/ko-sbert-multitask')
embeddings = model.encode(sentences)
print(embeddings)
```



## 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']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('jhgan/ko-sbert-multitask')
model = AutoModel.from_pretrained('jhgan/ko-sbert-multitask')

# 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)
```



## Evaluation Results

KorSTS, KorNLI ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ๋ฉ€ํ‹ฐ ํƒœ์Šคํฌ ํ•™์Šต์„ ์ง„ํ–‰ํ•œ ํ›„ KorSTS ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ์ž…๋‹ˆ๋‹ค.

- Cosine Pearson: 84.13
- Cosine Spearman: 84.71
- Euclidean Pearson: 82.42
- Euclidean Spearman: 82.66
- Manhattan Pearson: 81.41
- Manhattan Spearman: 81.69
- Dot Pearson: 80.05
- Dot Spearman: 79.69



## Training
The model was trained with the parameters:

**DataLoader**:

`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8885 with parameters:
```
{'batch_size': 64}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 5,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 360,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->
- Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korsts: New benchmark datasets for korean natural language understanding. arXiv
preprint arXiv:2004.03289
- Reimers, Nils and Iryna Gurevych. โ€œSentence-BERT: Sentence Embeddings using Siamese BERT-Networks.โ€ ArXiv abs/1908.10084 (2019)
- Reimers, Nils and Iryna Gurevych. โ€œMaking Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.โ€ EMNLP (2020).