Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,182 @@
|
|
1 |
-
---
|
2 |
-
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
tags:
|
4 |
+
- sentence-transformers
|
5 |
+
- feature-extraction
|
6 |
+
- sentence-similarity
|
7 |
+
- transformers
|
8 |
+
datasets:
|
9 |
+
- kornlu
|
10 |
+
language:
|
11 |
+
- ko
|
12 |
+
license: cc-by-4.0
|
13 |
+
---
|
14 |
+
|
15 |
+
# bi-matrix/gmatrix-embedding
|
16 |
+
|
17 |
+
ํด๋น ๋ชจ๋ธ์ [KF-DeBERTa](https://huggingface.co/kakaobank/kf-deberta-base) ๋ชจ๋ธ๊ณผ KorSTS, KorNLI ๋ฐ์ดํฐ์
์ ํ์ฉํ์์ผ๋ฉฐ, sentence-transformers์ ๊ณต์ ๋ฌธ์ ๋ด ์๊ฐ๋ [continue-learning](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) ๋ฐฉ๋ฒ์ ํตํด ์๋์ ๊ฐ์ด ํ์ต๋์์ต๋๋ค.
|
18 |
+
1. NLI ๋ฐ์ดํฐ์
์ ํตํด nagative sampling ํ MultipleNegativeRankingLoss ํ์ฉ ๋ฐ STS ๋ฐ์ดํฐ์
์ ํตํด CosineSimilarityLoss๋ฅผ ํ์ฉํ์ฌ Multi-task Learning ํ์ต 10epoch ์งํ
|
19 |
+
2. Learning Rate๋ฅผ 1e-06์ผ๋ก ์ค์ฌ์ 4epoch ์ถ๊ฐ Multi-task ํ์ต ์งํ
|
20 |
+
|
21 |
+
---
|
22 |
+
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.
|
23 |
+
|
24 |
+
<!--- Describe your model here -->
|
25 |
+
|
26 |
+
## Usage (Sentence-Transformers)
|
27 |
+
|
28 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
29 |
+
|
30 |
+
```
|
31 |
+
pip install -U sentence-transformers
|
32 |
+
```
|
33 |
+
|
34 |
+
Then you can use the model like this:
|
35 |
+
|
36 |
+
```python
|
37 |
+
from sentence_transformers import SentenceTransformer
|
38 |
+
sentences = ["This is an example sentence", "Each sentence is converted"]
|
39 |
+
|
40 |
+
model = SentenceTransformer("bi-matrix/gmatrix-embedding")
|
41 |
+
embeddings = model.encode(sentences)
|
42 |
+
print(embeddings)
|
43 |
+
```
|
44 |
+
|
45 |
+
|
46 |
+
|
47 |
+
## Usage (HuggingFace Transformers)
|
48 |
+
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.
|
49 |
+
|
50 |
+
```python
|
51 |
+
from transformers import AutoTokenizer, AutoModel
|
52 |
+
import torch
|
53 |
+
|
54 |
+
|
55 |
+
#Mean Pooling - Take attention mask into account for correct averaging
|
56 |
+
def mean_pooling(model_output, attention_mask):
|
57 |
+
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
58 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
59 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
60 |
+
|
61 |
+
|
62 |
+
# Sentences we want sentence embeddings for
|
63 |
+
sentences = ['This is an example sentence', 'Each sentence is converted']
|
64 |
+
|
65 |
+
# Load model from HuggingFace Hub
|
66 |
+
tokenizer = AutoTokenizer.from_pretrained("bi-matrix/gmatrix-embedding")
|
67 |
+
model = AutoModel.from_pretrained("bi-matrix/gmatrix-embedding")
|
68 |
+
|
69 |
+
# Tokenize sentences
|
70 |
+
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
71 |
+
|
72 |
+
# Compute token embeddings
|
73 |
+
with torch.no_grad():
|
74 |
+
model_output = model(**encoded_input)
|
75 |
+
|
76 |
+
# Perform pooling. In this case, mean pooling.
|
77 |
+
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
78 |
+
|
79 |
+
print("Sentence embeddings:")
|
80 |
+
print(sentence_embeddings)
|
81 |
+
```
|
82 |
+
|
83 |
+
|
84 |
+
## Evaluation Results
|
85 |
+
|
86 |
+
<!--- Describe how your model was evaluated -->
|
87 |
+
|
88 |
+
KorSTS ํ๊ฐ ๋ฐ์ดํฐ์
์ผ๋ก ํ๊ฐํ ๊ฒฐ๊ณผ์
๋๋ค.
|
89 |
+
|
90 |
+
- Cosine Pearson: 85.77
|
91 |
+
- Cosine Spearman: 86.30
|
92 |
+
- Manhattan Pearson: 84.84
|
93 |
+
- Manhattan Spearman: 85.33
|
94 |
+
- Euclidean Pearson: 84.82
|
95 |
+
- Euclidean Spearman: 85.29
|
96 |
+
- Dot Pearson: 83.19
|
97 |
+
- Dot Spearman: 83.19
|
98 |
+
|
99 |
+
<br>
|
100 |
+
|
101 |
+
|model|cosine_pearson|cosine_spearman|euclidean_pearson|euclidean_spearman|manhattan_pearson|manhattan_spearman|dot_pearson|dot_spearman|
|
102 |
+
|:-------------------------|-----------------:|------------------:|--------------------:|---------------------:|--------------------:|---------------------:|--------------:|---------------:|
|
103 |
+
|[**gmatrix-embedding**](https://huggingface.co/bi-matrix/gmatrix-embedding)|**85.77**|**86.30**|**84.82**|**85.29**|**84.84**|**85.33**|**83.19**|**83.19**|
|
104 |
+
|[kf-deberta-multitask](https://huggingface.co/upskyy/kf-deberta-multitask)|85.75|86.25|84.79|85.25|84.80|85.27|82.93|82.86|
|
105 |
+
|[ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask)|84.77|85.6|83.71|84.40|83.70|84.38|82.42|82.33|
|
106 |
+
|[ko-sbert-multitask](https://huggingface.co/jhgan/ko-sbert-multitask)|84.13|84.71|82.42|82.66|82.41|82.69|80.05|79.69|
|
107 |
+
|[ko-sroberta-base-nli](https://huggingface.co/jhgan/ko-sroberta-nli)|82.83|83.85|82.87|83.29|82.88|83.28|80.34|79.69|
|
108 |
+
|[ko-sbert-nli](https://huggingface.co/jhgan/ko-sbert-multitask)|82.24|83.16|82.19|82.31|82.18|82.3|79.3|78.78|
|
109 |
+
|[ko-sroberta-sts](https://huggingface.co/jhgan/ko-sroberta-sts)|81.84|81.82|81.15|81.25|81.14|81.25|79.09|78.54|
|
110 |
+
|[ko-sbert-sts](https://huggingface.co/jhgan/ko-sbert-sts)|81.55|81.23|79.94|79.79|79.9|79.75|76.02|75.31|
|
111 |
+
|
112 |
+
<br>
|
113 |
+
|
114 |
+
|
115 |
+
<!--- Describe how your model was evaluated -->
|
116 |
+
|
117 |
+
G-MATRIX Embedding ๋ฐ์ดํฐ์
์ธก์ ๊ฒฐ๊ณผ์
๋๋ค.
|
118 |
+
์ฌ๋ 3๋ช
์ด์ 0~5์ ์ผ๋ก ๋ ๋ฌธ์ฅ๊ฐ์ ์ ์ฌ๋๋ฅผ ์ธก์ ํ์ฌ ์ ์๋ฅผ ๋ด๊ณ ํ๊ท ๏ฟฝ๏ฟฝ ๊ตฌํ์ฌ ๊ฐ ๋ชจ๋ธ์ ์๋ฒ ๋ฉ๊ฐ์ ํตํด
|
119 |
+
|
120 |
+
์ฝ์ฌ์ธ ์ ์ฌ๋, ์ ํด๋ฆฌ๋์ ๊ฑฐ๋ฆฌ, ๋งจํํ ๊ฑฐ๋ฆฌ, Dot-product๋ฅผ ๊ตฌํ์ฌ ํผ์ด์จ, ์คํผ์ด๋ง ์๊ด๊ณ์๋ฅผ ๊ตฌํ ๊ฐ์
๋๋ค.
|
121 |
+
|
122 |
+
- Cosine Pearson: 75.86
|
123 |
+
- Cosine Spearman: 65.75
|
124 |
+
- Manhattan Pearson: 72.65
|
125 |
+
- Manhattan Spearman: 65.20
|
126 |
+
- Euclidean Pearson: 72.48
|
127 |
+
- Euclidean Spearman: 65.32
|
128 |
+
- Dot Pearson: 64.71
|
129 |
+
- Dot Spearman: 53.90
|
130 |
+
|
131 |
+
<br>
|
132 |
+
|
133 |
+
model|cosine_pearson|cosine_spearman|euclidean_pearson|euclidean_spearman|manhattan_pearson|manhattan_spearman|dot_pearson|dot_spearman|
|
134 |
+
|:-------------------------|-----------------:|------------------:|--------------------:|---------------------:|--------------------:|---------------------:|--------------:|---------------:|
|
135 |
+
|[**gmatrix-embedding**](https://huggingface.co/bi-matrix/gmatrix-embedding)|**75.86**|**65.75**|**72.65**|**65.20**|**72.48**|**65.32**|**64.71**|**53.90**|
|
136 |
+
|[ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask)|71.78|63.16|70.80|63.47|70.89|63.72|53.57|44.23|
|
137 |
+
|[bge-m3](https://huggingface.co/BAAI/bge-m3)|64.15|60.65|61.88|60.68|61.88|60.19|64.16|60.71|
|
138 |
+
|
139 |
+
<br>
|
140 |
+
|
141 |
+
|
142 |
+
|
143 |
+

|
144 |
+
|
145 |
+
<br>
|
146 |
+
|
147 |
+
## G-MATRIX Embedding ๋ ์ด๋ธ๋ง ํ๋จ ๊ธฐ์ค (KLUE-RoBERTa์ STS ๋ฐ์ดํฐ ์์ฑ ์ฐธ๊ณ )
|
148 |
+
1. ๋ ๋ฌธ์ฅ์ ์ ์ฌํ ์ ๋๋ฅผ ๋ณด๊ณ 0~5์ ์ผ๋ก ํ๋จ
|
149 |
+
2. ๋ง์ถค๋ฒ, ๋์ด์ฐ๊ธฐ, ์จ์ ์ด๋ ์ผํ ์ฐจ์ด๋ ํ๋จ ๋์์ด ์๋
|
150 |
+
3. ๋ฌธ์ฅ์ ์๋, ํํ์ด ๋ด๊ณ ์๋ ์๋ฏธ๋ฅผ ๋น๊ต
|
151 |
+
4. ๋ ๋ฌธ์ฅ์ ๊ณตํต์ ์ผ๋ก ์ฌ์ฉ๋ ๋จ์ด์ ์ ๋ฌด๋ฅผ ์ฐพ๋ ๊ฒ์ด ์๋, ๋ฌธ์ฅ์ ์๋ฏธ๊ฐ ์ ์ฌํ์ง๋ฅผ ๋น๊ต
|
152 |
+
5. 0์ ์๋ฏธ์ ์ ์ฌ์ฑ์ด ์๋ ๊ฒฝ์ฐ์ด๊ณ , 5๋ ์๋ฏธ์ ์ผ๋ก ๋๋ฑํจ์ ๋ปํจ
|
153 |
+
|
154 |
+
|
155 |
+
|
156 |
+
## Training
|
157 |
+
The model was trained with the parameters:
|
158 |
+
|
159 |
+
**DataLoader**:
|
160 |
+
|
161 |
+
`torch.utils.data.dataloader.DataLoader` of length 329 with parameters:
|
162 |
+
```
|
163 |
+
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
|
164 |
+
```
|
165 |
+
|
166 |
+
**Loss**:
|
167 |
+
|
168 |
+
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
|
169 |
+
|
170 |
+
|
171 |
+
## Full Model Architecture
|
172 |
+
```
|
173 |
+
SentenceTransformer(
|
174 |
+
(0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: DeBERTaV2Model
|
175 |
+
(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})
|
176 |
+
)
|
177 |
+
```
|
178 |
+
|
179 |
+
## Citing & Authors
|
180 |
+
|
181 |
+
<!--- Describe where people can find more information -->
|
182 |
+
[MINSANG SONG] at [BI-Matrix](https://www.bimatrix.co.kr/)
|