Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +10 -0
- README.md +870 -0
- config.json +29 -0
- config_sentence_transformers.json +10 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +66 -0
- vocab.txt +0 -0
1_Pooling/config.json
ADDED
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
ADDED
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@@ -0,0 +1,870 @@
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| 1 |
+
---
|
| 2 |
+
base_model: klue/roberta-base
|
| 3 |
+
library_name: setfit
|
| 4 |
+
metrics:
|
| 5 |
+
- metric
|
| 6 |
+
pipeline_tag: text-classification
|
| 7 |
+
tags:
|
| 8 |
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- setfit
|
| 9 |
+
- sentence-transformers
|
| 10 |
+
- text-classification
|
| 11 |
+
- generated_from_setfit_trainer
|
| 12 |
+
widget:
|
| 13 |
+
- text: (๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ ํด๋ ์๋ฅด
|
| 14 |
+
- text: ๋กฏ๋ฐ์น ์ฑ์๋ฃ ์น ์ฑ ์ฌ์ด๋ค 355ml 12๊ฐ์
์คํํธ_์ ๋ก ์ฝ์นด์ฝ๋ผ 500ml 24๊ฐ์
์น๋ฏผ๋ชฐ
|
| 15 |
+
- text: ํํํฐ์์ ์ผ์ดํฐ๋ง ์์ผํฉ15์ข
์ธ์ฒ์ถ์ฅ๋ทํ ์ง๋ค์ด ๋ฐฐ๋ฌ ๋์์น์ ์๊ท๋ชจ ์๋์ด๋์๋ฆฌ ์์ผํฉ15์ข
(์ผํ์ฉ๊ธฐ) -20000์ํ ์ธ_12์_2์ผ
|
| 16 |
+
(์ฃผ)์
๋ฃจ์ฒด
|
| 17 |
+
- text: 50๋
์ ํต ๋ํ์ํ ์ ์จ์์ฐฉ ์ฐธ๊ธฐ๋ฆ 350ml / ๋งค์ผ์ฐฉ์ ๋ฐฉ์๊ฐ 10.๊ฒ์๊นจ์ฐธ๊ธฐ๋ฆ180ml ์ฃผ์ํ์ฌ ๋ํ์ํ
|
| 18 |
+
- text: ๋ดํฐ ํฌ์ผ๋ชฌ ์ฝค๋ถ์ฐจ ์ค์ธ๋จธ์ค์บฃ 10T +์ปคํผ๋ฏน์ค ์คํฑ 2T ์ค๋ ์ค ๊ณผ์_๋ฆฌ์ธ ํฌ๋์ปค ์น์ฆ 96g ์ฃผ์ํ์ฌ ๊ฒฝ์ผ์ข
ํฉ์ํ ์ผ์ด๋งํธ๋ชฐ
|
| 19 |
+
inference: true
|
| 20 |
+
model-index:
|
| 21 |
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- name: SetFit with klue/roberta-base
|
| 22 |
+
results:
|
| 23 |
+
- task:
|
| 24 |
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type: text-classification
|
| 25 |
+
name: Text Classification
|
| 26 |
+
dataset:
|
| 27 |
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name: Unknown
|
| 28 |
+
type: unknown
|
| 29 |
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split: test
|
| 30 |
+
metrics:
|
| 31 |
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- type: metric
|
| 32 |
+
value: 0.9219075463944795
|
| 33 |
+
name: Metric
|
| 34 |
+
---
|
| 35 |
+
|
| 36 |
+
# SetFit with klue/roberta-base
|
| 37 |
+
|
| 38 |
+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
|
| 39 |
+
|
| 40 |
+
The model has been trained using an efficient few-shot learning technique that involves:
|
| 41 |
+
|
| 42 |
+
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
|
| 43 |
+
2. Training a classification head with features from the fine-tuned Sentence Transformer.
|
| 44 |
+
|
| 45 |
+
## Model Details
|
| 46 |
+
|
| 47 |
+
### Model Description
|
| 48 |
+
- **Model Type:** SetFit
|
| 49 |
+
- **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
|
| 50 |
+
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
| 51 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 52 |
+
- **Number of Classes:** 22 classes
|
| 53 |
+
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
|
| 54 |
+
<!-- - **Language:** Unknown -->
|
| 55 |
+
<!-- - **License:** Unknown -->
|
| 56 |
+
|
| 57 |
+
### Model Sources
|
| 58 |
+
|
| 59 |
+
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
|
| 60 |
+
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
|
| 61 |
+
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
| 62 |
+
|
| 63 |
+
### Model Labels
|
| 64 |
+
| Label | Examples |
|
| 65 |
+
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 66 |
+
| 9.0 | <ul><li>'๋ช
์ธ ํ์๋ฆฌ ์ฒญ๋งค์ค๋์ ๊ณ ์ถ์ฅ์ฅ์์ฐ 430g ๋ฐฅ๋ฐ์ฐฌ ๋ฐ๋ฐ์ฐฌ ์งฑ์์ฐ ์งฑ์์น ๋ฃฐ๋ฃจ๋๋ผ'</li><li>'๋งํ๋ ์ด ๋น๋จ์๋จ ์ฃผ 1ํ ๋ฐฐ์ก(๊ฑด๊ฐ์ ๋ฐ์ฐฌ ์๋จํ ๊ฑด๊ฐ์๋จ ๋ฐฐ๋ฌ ์ ๋น์) 1๊ฐ์ ๊ตฌ๋
์ถ๊ฐ์ํจ +0์_๋ฐ์ฐฌ ์ถ๊ฐ +37000์ ๊ฐ๋์'</li><li>'์ฒญ์ฐ ์ง์ง ๋ง์๋ ๋๋ ๋ฌด์นจ 4kg ๊ตญ๋ด์ ์กฐ ๋๋๋ฌด์นจ 4kg ์ฃผ์ํ์ฌ ์ฒญ์ฐ์ํ'</li></ul> |
|
| 67 |
+
| 0.0 | <ul><li>'๋ฒ ์ด๋น๋ฝ 19์ข
์๊ธฐ์ ์ฐ๊ท ์๊ตฌ๋ฅดํธ๋ง ์์ฐํจ์ 2๊ฐ์๋ถ ๋ ์ ์ ์ด๋ฆฐ์ด 5.(์ก์๋นํ๋ฏผD) ํกํก ๋๋กญํ์ ์ฐ๊ท 1BOX ์ฃผ์ํ์ฌ ํ๋์ฐ์ค(Honeywells Co., Ltd.)'</li><li>'์ฌ์กฐํดํ ์ฐน์๊ฐ๋ฃจ 350g ๊ฑด์ฐํธ๋'</li><li>'๋ฐ๋ ฅ ์๊ฐ๋ฃจ ๊ตญ์ฐ 3kg ํ์๋ง๋ฃจ ๋๋ ๋ง์f&b'</li></ul> |
|
| 68 |
+
| 2.0 | <ul><li>'ํฌ์คํธ ์ฝํธ๋ผ์ดํธ600g ํฌ์คํธ ์ฝํธ๋ผ์ดํธ600g+์ค๋ ์ค์ค์ฆ30g ์ ์๋ฌผ๋ฅ'</li><li>'๋ฏธ์ฑํจ๋ฐ๋ฆฌ ํธ๋กํผ์นผ ํธ๋ฉ 708g ์ค์'</li><li>'ํ์์ํ์ค์ง์ด์๋ค๋ฆฌ 20g 10๊ฐ ์ฃผ์ํ์ฌ ๋ฐ๋ '</li></ul> |
|
| 69 |
+
| 12.0 | <ul><li>'์๋ฆฌ๋ ํด๋ฐ๋ผ๊ธฐ์จ์ ์ ๋ฌผ์ธํธ 250ml 2๋ณ ์ผํ์ฌ๊ฑฐ๋ฆฌ'</li><li>'cj ๋ฐฑ์ค ์ ์ผ์ ๋น ์ฝฉ๊ธฐ๋ฆ1.8L ํฌ๋์จ์ 900ml ํ๊ฒฐ๋ง์ผ'</li><li>'์ค๋๊ธฐ ํ๋ ์ค์ฝ ์นด๋๋ผ์ 900ml (์ฃผ) ์ฝ๋ฆฌ์์. ์ . ์จ'</li></ul> |
|
| 70 |
+
| 6.0 | <ul><li>'๋ฉ์ดํํธ๋ฆฌ ๊ฐ๋ฅด์๋์ ํ๋ฌ์คํธ๋ฆฌํ 800mgx112์ ๋ฌ๋ธ๋ฆฌ๋ฃจ๋'</li><li>'๋จธ์ฌํ
ํฌ ์์ผ์
์๋ฆฌ์ฆ ํ๋ํฐ๋ ํฌ๋ ์ํด ๋ฌด๋ง 400g 14.11oz ๋์ ์ด์ต'</li><li>'[3์ 25์ผ ์ถ์] ๋ฐ์ดํ๋ทฐํฐ ๋ฉํ๊ทธ๋ฆฐ ์ฌ๋ฆผ์
30์ผ new ๋ฉํ๊ทธ๋ฆฐ์ฌ๋ฆผ์
(์ฃผ)์๋ชจ๋ ํผ์ํฝ'</li></ul> |
|
| 71 |
+
| 18.0 | <ul><li>'์ฒญ์ ์ํ 23๋
๊ตญ์ฐ ๊ณ ์ด ํ ๊ณ ์ถง๊ฐ๋ฃจ 1kg CJA01-9_(์ฒญ์)๊ตต์ ๊ณ ์ถ๊ฐ๋ฃจ 1kg ์ ํํด๋ฒ๋ฆฌ ์์ค์์ด'</li><li>'๋ฐฑ์ค ์๋ฆฌ๋น 5kg ๋น์ผ ์ถ๋ฐ (์ฃผ) ๋ฐ์ฟฐ'</li><li>'ํ์ ๊ฐ์์คํ 15KG ์ง์ฐFun'</li></ul> |
|
| 72 |
+
| 4.0 | <ul><li>'์๊ด๋๋ก๋ณถ์ด ๊ธฐ๋ฆ์๋ก๋ณถ์ด 1ํฉ (์ฃผ)์ค๋ฆฌ์ํธํธ๋'</li><li>'๋ญ์ปค ์์ค ๋ญ๊ฐ์ด์ด ๋์๋ฝ ๊ฐ๋ฆญ์คํ
์ดํฌ 250g X 20ํฉ / ์๋จ ์ง์ฅ์ธ ์ ์ฌ ๊ณ ๋จ๋ฐฑ ์งฌ๋ฝ 20ํฉ (์ฃผ)ํธ๋๋๋ฌด'</li><li>'CJ ์์ด๊ฐ์ ๊ณ ๋จ๋ฐฑ ๊ฑด๊ฐํ๊ฐ์ CJ ๊ณ ๋จ๋ฐฑ์ ๋น ๋์๋ฝ 500 ๋ฟ๋ฆฌ์ฑ์์ฐ๋ญ 404g ํ๋ผ์์ฌ ํธ์งํ๋ฐ์ฐฌ ์บ ํ์๋ฆฌ ์ง๋๋ง์ผ'</li></ul> |
|
| 73 |
+
| 21.0 | <ul><li>'๋์์ํ์ค๋น ๋์์ฐธ์น ์ด์ฝ๊ธฐ ์ธ ์ํฐ 100g 1๊ฐ 1๊ฐ(์ฌํฌ์ฅ์ผ๋ก ์ธํ ๋ฐฐ์ก์ง์ฐ ๋ฐ์ ๊ฐ๋ฅ) ํ
์ผ์ข
ํฉ์์ฌ'</li><li>'๋์์ํ ๋์ ๋ฆฌ์น์ค ์ฌ๋ผ์ด์ค ๋ธ๋์ฌ๋ฆฌ๋ธ 3kg ์ฃผ์ํ์ฌ ๋์'</li><li>'๋์์ฐธ์น ํ๋๋ฆฌ์ฑ O-48ํธ ์ ๋ฌผ์ธํธ ๋ช
์ ์ ๋ฌผ์ธํธ (์ฃผ)๊ณจ๋ ๋ง๋ '</li></ul> |
|
| 74 |
+
| 17.0 | <ul><li>'์
ฐํ๋ง์คํฐ ์ํ๋ง์คํฐ ์์ฉ์์ 0.7oz ๋ฆฌ์ฟ ์์ ค ๋ง์นด๋กฑ์์ ๋ฐ์ก์ํ์
๋ฏผํธ๊ทธ๋ฆฐ ์๋ฒ ์ดํฌ'</li><li>'ํ์ ์ํ๋ฏน์ค2 10kg / ์ค๊ทธ๋ผ์ด๋'</li><li>'์จ์์ฐน์ํธ๋ก 100g 10๊ฐ ๊ฒจ์ธ ์์ด๊ฐ์ ํ์ธ์ํ'</li></ul> |
|
| 75 |
+
| 20.0 | <ul><li>'ํ์ผ์์ ์ก(๋ผ์์ด) ๋ถ์์ ํ 500g ๋ท๋ค๋ฆฌ์ด์์ก์ฉ(๊ป์งํฌํจ/์ฐ์ง์์) 500g ์ ๋ด์ ํต'</li><li>'๋ง์์ผ๋ฉด ์ง์ง ํ๋ถ, ํธ์ฃผ์ฐ ํ๋ ์น๋ ์๊ณ ๊ธฐ ์๊ป๋จธ๊ธ๋จ'</li><li>'(๊ณ ๊ธฐ์ฒ๊ตญ) ์๋ชฉ์ด 400g ๋ชฉ์ ์ง ์ผ๊ฒน์ด ๋ํจ์ผ๊ฒน์ด ๋ณด์์ฉ ์บ ํ์ฉ ์์ดํ๋ผ์ด์ด 04.๊ณ ๊ธฐ์ฒ๊ตญ ์ผ๊ฒน์ด(๊ตฌ์ด์ฉ)400g ๋์
ํ์ฌ๋ฒ์ธ ์ฃผ์ํ์ฌ ํด๋๋ฆผ'</li></ul> |
|
| 76 |
+
| 15.0 | <ul><li>'์ผํ์ฉ ๊ผฌ๋ง์ถ์ฅ 6g 400๊ฐ์
์ฌ์ํ ์ค์ฐฌ๋ช
๊ฐ ํด๋์ฉ ์ถ์ฅ ์์ด์ฆ์ฐ'</li><li>'๋ง๋ฅ ์๋ฆฌ์ฃผ ( ๋ง์ ์๋ฆฌ์ ๋ฏธํฅ ๋ฏธ๋ฆฐ ์ง์ง ๋ฏธ๋ฆผ ) ํผ๋ฏธ๋ฆฐ 1L (ํํธ) x 12๊ฐ (๋ฐ์ค) ๋ํ๋ฌด์ํ์ด ํฅ๋ณต์ฑ'</li><li>'๋ฐ์ค ๋ฐฑ๋์ฅ ๋ง์ฐ๋ชฝ๊ณ 4K ํ์ธ์ ์จ๋'</li></ul> |
|
| 77 |
+
| 14.0 | <ul><li>'์ฝ์นด์ฝ๋ผ ์จ๊ทธ๋จ ๋ผ๋ฒจํ๋ฆฌ ํ๋ ์ธ 350ml 24๊ฐ ๋ ์๋ณด์๋ฒจ๋ฅด๋น'</li><li>'๋ดํฐ ํซ์ด์ฝ 50T ์ฃผ์ํ์ฌ ํตํต๋งํธ'</li><li>'์ ํฐ ์ด์ฝ๋ ๋ง 175ml x 30์บ ๋จ์-๊ณผ์์์ค๋ ์ง190ml x24 (์ฃผ)์ปคํผ๋ชฐ'</li></ul> |
|
| 78 |
+
| 10.0 | <ul><li>'ํฐํ๋ ์ค๋ฆฌ์ํ ๋๋ ์ฑ 270g ์ฃผ์ํ์ฌ ๋ค๋ค๋ฆฐ๋'</li><li>'์ค๋๊ธฐ ๊ฒฝ์์ ๋๊น์ค์์ค 455g ์์ด์น๋ธ์ด๋ง์ผ'</li><li>'์ค๋๊ธฐ ํ ๋งํ ์ผ์ฐน 500g 1๊ฐ ๊ตฌ๋์ฆ๋ชฐ'</li></ul> |
|
| 79 |
+
| 16.0 | <ul><li>'1883 ๋ฐ๋๋ผ ์๋ฝ ์นดํ 1022033 23.์ํฐ๋์นด๋ผ๋ฉ ์๋ฝ ์ค๋์๋'</li><li>'ํฌ์ฐฝ ์ ๋ก์คํ ์นดํ์๋ฝ 1.5L ์ปคํผ์๋ ์ ๋ก์คํ ์ ๋ก์๋ฝ ์นดํ์ฉํ ๋ธ๋ก ์ค์ฝ๋ฆฌ์(์ฃผ)'</li><li>'1883 ๋ฐ๋๋ผ์๋ฝ 1000ml ์นดํ 1022247 1883 ์๋๋ชฌ์๋ฝ 1000ml ์ค๋์๋'</li></ul> |
|
| 80 |
+
| 1.0 | <ul><li>'biild ๋น๋ ํ๋ฆฌ๋ฏธ์ ํ
ํ ๋ฐํจ ํจ์ 30ํฌ x 1๋ฐ์ค ํ๋ผ์์ปค๋จธ์ค'</li><li>'์ํ์ ์์์ ์ข
๊ทผ๋น๊ฑด๊ฐ Dr.Care ์บ์์ฝ์น 200mlX18๊ฐ 18๊ฐ ์ข
๊ทผ๋น๊ฑด๊ฐ(์ฃผ)'</li><li>'๋์์ฐ๋ผ์ดํ ๋ด์ผ์ด ์ธํธ์ 200ml 1ํฉ ์ฃผ์ํ์ฌ ์์ด์ง๋น'</li></ul> |
|
| 81 |
+
| 13.0 | <ul><li>'๋ฐ๋ ๋๋ฌผ์ฑ ํํํฌ๋ฆผ ์ ์ง๋ฐฉ 35ํ๋ก MILRAM WHIPPING CREAM ์ฃผ์ํ์ฌ ์ฝฉ์ฝฉ'</li><li>'๋งค์ผ ์ฐ์ ์ํํธ 1๊ฐ 500g ๊ฐ๋น์ฐ์ ๋งค์ผ ์ฐ์ 500g(๋นจ๊ฐ) x 1๊ฐ ๋งค์ผ์ ์
(์ฃผ) ์กฐ์น์๋๋ฆฌ์ '</li><li>'๋ณด๋ผํฐ์ ํ๋ ์ง๋ฉ ๋ฌด์ผ๋ฒํฐํฉ 400g x 20๊ฐ ๋ฐ๊ธธ์ฉ'</li></ul> |
|
| 82 |
+
| 3.0 | <ul><li>'ํ์์ ๋ด์ ๊น์น] ์ค์ด์๋ฐ์ด ๊ตญ์ฐ๊น์น 5kg (์ฃผ)์ง์ ์ธํฌํ
'</li><li>'๋น๋น๊ณ ์ด๊ฐ๊น์น 900g ๋ฏธ๋ฃจ์์คํ
'</li><li>'์์๋ด ํน๋ฌต์์ง3kg ์์๋ด ํน๋ฌต์์ง3kg ์์ธ์์ฑ '</li></ul> |
|
| 83 |
+
| 8.0 | <ul><li>'์๋์ด๋์๋ฆฌ ๋งค์ฝค ๋น ๋ค ํฌ๋ฆผ ํ์คํ ๋ฐํคํธ 2์ธ๋ถ ์ฃผ์ํ์ฌ ์ ์ผ์จ์์ํ(C&F)'</li><li>'๊ธฐ์ฅ๋์ง ์ค์ง ์ ๋ณต์ฃฝ ๋ฐํคํธ ์ ๋ณต๋ด์ฅ ๊ฐํธ ์์์ฃฝ 6์ 7์ผ(๊ธ) ์ฃผ์ํ์ฌ ๊ธฐ์ฅ๋์ง'</li><li>'ํผ์ง์ค๏ฟฝ๏ฟฝ๊ทธ ๋ฐ์ค์ผ์ดํฐ๋ง ์์ธ๊ฒฝ๊ธฐ ์ธ๋ฏธ๋ ์์ผํํฐ ์คํ์ ์ผ์ดํฐ๋ง 07. ์ดํ๋ฆฌ์๋จธ์ฌ๋ฃธ์น์๋ฐํ 10pcs ํผ์ง์คํผ๊ทธ'</li></ul> |
|
| 84 |
+
| 11.0 | <ul><li>'๋ํ๋ช
๋ ์ ์ผ ์์ ์์ง์ฐ๋ํธ๋ฅจ์๋ ๊ฐํธํ๊ฒ ์๋ก๋ง ๋ฐ๋ผ๋ธ ํ๋ธ ๋ช
๋์ 1+1ํฉ 220g 4. ์ก์์ก์๋ช
๋ 1ํฉ_3. ์๋ฅธ๋ช
๋ 1ํฉ (์ฃผ)๋์ค๋ป'</li><li>'๋ง์ผํ๋ก์ฆ ์์ดํ๋ผ์ด์ด์ฉ ํ๊น ๋๊น์ค ์์ฐ๊น์ค ๊ณจ๋ผ๋ด๊ธฐ ํ๊ฒจ๋์จ 60์์ฐ 320g (์ฃผ)๋๋ธํผ์ฌ์ปค๋ฎค๋์ผ์ด์
์ฆ'</li><li>'[๊ฐ์๋ฐ๋ค] ๋ถ๋๋ฌ์ด ๋ง๋ฅธ์ค์ง์ด 20๋ง๋ฆฌ (๊ฑด์ค์ง์ด) ๋ง๋ฅธ์ค์ง์ด_20๋ง๋ฆฌ 1.7kg ๊ฐ์๋ฐ๋ค'</li></ul> |
|
| 85 |
+
| 7.0 | <ul><li>'๋ฉด์ฌ๋ ์ซ๋ฉด 2kg 220647 ์ฒญ์ฐ๋ฆผ ์ซ๋ฉด 2KG ๋จธ์น๋ฐ์'</li><li>'๋์ฌ ์งํ๊ฒํฐ ๋๋ธ๋ ๋ด์ง๋ผ๋ฉด 116g x 4๊ฐ ๋ํ๋ฆฌํ
์ผ'</li><li>'๋์ฌ ํ๊น์ฐ๋ ํฐ์ฌ๋ฐ 111g x16์
์ด๋๋ฐ ์ฃผ์ํ์ฌ๊ฑด์์ ํต'</li></ul> |
|
| 86 |
+
| 5.0 | <ul><li>'GAP์ธ์ฆ ๋์ฃผ ์ ๊ณ ๋ฐฐ ํนํ 7.5kg (์ ๋ฌผ์ฉ, ์ ์์ฉ) ๋์ฃผ๋ฐฐ 7.5kg(7~10๊ณผ) ๊ณ ๊ธ์ ๋ฌผ์ฉ ์ฃผ์ํ์ฌ ์ ๋๋์ค'</li><li>'๋ฐค๋จ๋ ์ฐ๋ฆฌ๋๋ผ ๋ง๋ฐค 50g ๊ณต์ฃผ ์์จ๋ฐค ๋ถ์ฌ ์ฝ๋จ๋ฐค ํ๋ฐค ์ฐ๋ฆฌ๋๋ผ๋ง๋ฐค 50g ์ ์ด์ผ์ด์ปดํผ๋'</li><li>'[์ฒญ์ค] ์ ๊ธฐ๋ ๋ฐ์ 10๊ณก ํผํฉ๊ณก 800g (๊ตญ์ฐ์ก๊ณก) ์ด๋๋ผ๋ค'</li></ul> |
|
| 87 |
+
| 19.0 | <ul><li>'์ ๋น ์ง ์ ์ฃผ ์๋์
48๋ 750ml ํ ๋ผ์์ฃผ ํด์น์์ฃผ ํ๊ตญ ์์ฃผ ํ์ด๋ณผ ์์ฃผ ๋ธ๊พน ์ง๋งค์ฅ'</li><li>'์ ์ฃผ์ ์ ์ฃผ ์ค๋ฉ๊ธฐ์ 13๋ 375ml ์ฝ์ฃผ ๋ธ๊พน ์ง๋งค์ฅ'</li><li>'์ ํฝ ํ ๋ผ์์ฃผ ๋ธ๋ 40๋ 750ml TOKKI SOJU BLACK ์ ๋ฒ์ปค ์ฃผ์ํ์ฌ ๋์
ํ์ฌ๋ฒ์ธ ์ด์ฒ์ง์ '</li></ul> |
|
| 88 |
+
|
| 89 |
+
## Evaluation
|
| 90 |
+
|
| 91 |
+
### Metrics
|
| 92 |
+
| Label | Metric |
|
| 93 |
+
|:--------|:-------|
|
| 94 |
+
| **all** | 0.9219 |
|
| 95 |
+
|
| 96 |
+
## Uses
|
| 97 |
+
|
| 98 |
+
### Direct Use for Inference
|
| 99 |
+
|
| 100 |
+
First install the SetFit library:
|
| 101 |
+
|
| 102 |
+
```bash
|
| 103 |
+
pip install setfit
|
| 104 |
+
```
|
| 105 |
+
|
| 106 |
+
Then you can load this model and run inference.
|
| 107 |
+
|
| 108 |
+
```python
|
| 109 |
+
from setfit import SetFitModel
|
| 110 |
+
|
| 111 |
+
# Download from the ๐ค Hub
|
| 112 |
+
model = SetFitModel.from_pretrained("mini1013/master_item_fd")
|
| 113 |
+
# Run inference
|
| 114 |
+
preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ ํด๋ ์๋ฅด")
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
<!--
|
| 118 |
+
### Downstream Use
|
| 119 |
+
|
| 120 |
+
*List how someone could finetune this model on their own dataset.*
|
| 121 |
+
-->
|
| 122 |
+
|
| 123 |
+
<!--
|
| 124 |
+
### Out-of-Scope Use
|
| 125 |
+
|
| 126 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 127 |
+
-->
|
| 128 |
+
|
| 129 |
+
<!--
|
| 130 |
+
## Bias, Risks and Limitations
|
| 131 |
+
|
| 132 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 133 |
+
-->
|
| 134 |
+
|
| 135 |
+
<!--
|
| 136 |
+
### Recommendations
|
| 137 |
+
|
| 138 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 139 |
+
-->
|
| 140 |
+
|
| 141 |
+
## Training Details
|
| 142 |
+
|
| 143 |
+
### Training Set Metrics
|
| 144 |
+
| Training set | Min | Median | Max |
|
| 145 |
+
|:-------------|:----|:-------|:----|
|
| 146 |
+
| Word count | 3 | 9.1898 | 30 |
|
| 147 |
+
|
| 148 |
+
| Label | Training Sample Count |
|
| 149 |
+
|:------|:----------------------|
|
| 150 |
+
| 0.0 | 448 |
|
| 151 |
+
| 1.0 | 579 |
|
| 152 |
+
| 2.0 | 800 |
|
| 153 |
+
| 3.0 | 552 |
|
| 154 |
+
| 4.0 | 1049 |
|
| 155 |
+
| 5.0 | 350 |
|
| 156 |
+
| 6.0 | 800 |
|
| 157 |
+
| 7.0 | 100 |
|
| 158 |
+
| 8.0 | 400 |
|
| 159 |
+
| 9.0 | 414 |
|
| 160 |
+
| 10.0 | 581 |
|
| 161 |
+
| 11.0 | 275 |
|
| 162 |
+
| 12.0 | 450 |
|
| 163 |
+
| 13.0 | 300 |
|
| 164 |
+
| 14.0 | 600 |
|
| 165 |
+
| 15.0 | 422 |
|
| 166 |
+
| 16.0 | 400 |
|
| 167 |
+
| 17.0 | 200 |
|
| 168 |
+
| 18.0 | 571 |
|
| 169 |
+
| 19.0 | 50 |
|
| 170 |
+
| 20.0 | 346 |
|
| 171 |
+
| 21.0 | 450 |
|
| 172 |
+
|
| 173 |
+
### Training Hyperparameters
|
| 174 |
+
- batch_size: (512, 512)
|
| 175 |
+
- num_epochs: (20, 20)
|
| 176 |
+
- max_steps: -1
|
| 177 |
+
- sampling_strategy: oversampling
|
| 178 |
+
- num_iterations: 40
|
| 179 |
+
- body_learning_rate: (2e-05, 2e-05)
|
| 180 |
+
- head_learning_rate: 2e-05
|
| 181 |
+
- loss: CosineSimilarityLoss
|
| 182 |
+
- distance_metric: cosine_distance
|
| 183 |
+
- margin: 0.25
|
| 184 |
+
- end_to_end: False
|
| 185 |
+
- use_amp: False
|
| 186 |
+
- warmup_proportion: 0.1
|
| 187 |
+
- seed: 42
|
| 188 |
+
- eval_max_steps: -1
|
| 189 |
+
- load_best_model_at_end: False
|
| 190 |
+
|
| 191 |
+
### Training Results
|
| 192 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
| 193 |
+
|:-------:|:-----:|:-------------:|:---------------:|
|
| 194 |
+
| 0.0006 | 1 | 0.426 | - |
|
| 195 |
+
| 0.0316 | 50 | 0.3663 | - |
|
| 196 |
+
| 0.0631 | 100 | 0.4116 | - |
|
| 197 |
+
| 0.0947 | 150 | 0.3355 | - |
|
| 198 |
+
| 0.1263 | 200 | 0.3032 | - |
|
| 199 |
+
| 0.1578 | 250 | 0.2642 | - |
|
| 200 |
+
| 0.1894 | 300 | 0.2304 | - |
|
| 201 |
+
| 0.2210 | 350 | 0.1933 | - |
|
| 202 |
+
| 0.2525 | 400 | 0.1744 | - |
|
| 203 |
+
| 0.2841 | 450 | 0.1711 | - |
|
| 204 |
+
| 0.3157 | 500 | 0.1426 | - |
|
| 205 |
+
| 0.3472 | 550 | 0.1373 | - |
|
| 206 |
+
| 0.3788 | 600 | 0.1259 | - |
|
| 207 |
+
| 0.4104 | 650 | 0.1016 | - |
|
| 208 |
+
| 0.4419 | 700 | 0.1094 | - |
|
| 209 |
+
| 0.4735 | 750 | 0.0845 | - |
|
| 210 |
+
| 0.5051 | 800 | 0.0845 | - |
|
| 211 |
+
| 0.5366 | 850 | 0.0689 | - |
|
| 212 |
+
| 0.5682 | 900 | 0.0659 | - |
|
| 213 |
+
| 0.5997 | 950 | 0.0526 | - |
|
| 214 |
+
| 0.6313 | 1000 | 0.0547 | - |
|
| 215 |
+
| 0.6629 | 1050 | 0.0446 | - |
|
| 216 |
+
| 0.6944 | 1100 | 0.0508 | - |
|
| 217 |
+
| 0.7260 | 1150 | 0.0388 | - |
|
| 218 |
+
| 0.7576 | 1200 | 0.0322 | - |
|
| 219 |
+
| 0.7891 | 1250 | 0.0257 | - |
|
| 220 |
+
| 0.8207 | 1300 | 0.0236 | - |
|
| 221 |
+
| 0.8523 | 1350 | 0.0226 | - |
|
| 222 |
+
| 0.8838 | 1400 | 0.0191 | - |
|
| 223 |
+
| 0.9154 | 1450 | 0.0287 | - |
|
| 224 |
+
| 0.9470 | 1500 | 0.0254 | - |
|
| 225 |
+
| 0.9785 | 1550 | 0.0181 | - |
|
| 226 |
+
| 1.0101 | 1600 | 0.0272 | - |
|
| 227 |
+
| 1.0417 | 1650 | 0.0203 | - |
|
| 228 |
+
| 1.0732 | 1700 | 0.0191 | - |
|
| 229 |
+
| 1.1048 | 1750 | 0.0252 | - |
|
| 230 |
+
| 1.1364 | 1800 | 0.0221 | - |
|
| 231 |
+
| 1.1679 | 1850 | 0.0147 | - |
|
| 232 |
+
| 1.1995 | 1900 | 0.0184 | - |
|
| 233 |
+
| 1.2311 | 1950 | 0.0142 | - |
|
| 234 |
+
| 1.2626 | 2000 | 0.0032 | - |
|
| 235 |
+
| 1.2942 | 2050 | 0.0144 | - |
|
| 236 |
+
| 1.3258 | 2100 | 0.0126 | - |
|
| 237 |
+
| 1.3573 | 2150 | 0.0145 | - |
|
| 238 |
+
| 1.3889 | 2200 | 0.0119 | - |
|
| 239 |
+
| 1.4205 | 2250 | 0.006 | - |
|
| 240 |
+
| 1.4520 | 2300 | 0.0164 | - |
|
| 241 |
+
| 1.4836 | 2350 | 0.0054 | - |
|
| 242 |
+
| 1.5152 | 2400 | 0.0074 | - |
|
| 243 |
+
| 1.5467 | 2450 | 0.0079 | - |
|
| 244 |
+
| 1.5783 | 2500 | 0.0058 | - |
|
| 245 |
+
| 1.6098 | 2550 | 0.0059 | - |
|
| 246 |
+
| 1.6414 | 2600 | 0.0041 | - |
|
| 247 |
+
| 1.6730 | 2650 | 0.0076 | - |
|
| 248 |
+
| 1.7045 | 2700 | 0.0023 | - |
|
| 249 |
+
| 1.7361 | 2750 | 0.003 | - |
|
| 250 |
+
| 1.7677 | 2800 | 0.0047 | - |
|
| 251 |
+
| 1.7992 | 2850 | 0.001 | - |
|
| 252 |
+
| 1.8308 | 2900 | 0.0006 | - |
|
| 253 |
+
| 1.8624 | 2950 | 0.0012 | - |
|
| 254 |
+
| 1.8939 | 3000 | 0.0011 | - |
|
| 255 |
+
| 1.9255 | 3050 | 0.0016 | - |
|
| 256 |
+
| 1.9571 | 3100 | 0.0009 | - |
|
| 257 |
+
| 1.9886 | 3150 | 0.0008 | - |
|
| 258 |
+
| 2.0202 | 3200 | 0.0015 | - |
|
| 259 |
+
| 2.0518 | 3250 | 0.0008 | - |
|
| 260 |
+
| 2.0833 | 3300 | 0.001 | - |
|
| 261 |
+
| 2.1149 | 3350 | 0.0014 | - |
|
| 262 |
+
| 2.1465 | 3400 | 0.002 | - |
|
| 263 |
+
| 2.1780 | 3450 | 0.0002 | - |
|
| 264 |
+
| 2.2096 | 3500 | 0.0051 | - |
|
| 265 |
+
| 2.2412 | 3550 | 0.0004 | - |
|
| 266 |
+
| 2.2727 | 3600 | 0.0003 | - |
|
| 267 |
+
| 2.3043 | 3650 | 0.0003 | - |
|
| 268 |
+
| 2.3359 | 3700 | 0.0005 | - |
|
| 269 |
+
| 2.3674 | 3750 | 0.0004 | - |
|
| 270 |
+
| 2.3990 | 3800 | 0.0004 | - |
|
| 271 |
+
| 2.4306 | 3850 | 0.0004 | - |
|
| 272 |
+
| 2.4621 | 3900 | 0.0006 | - |
|
| 273 |
+
| 2.4937 | 3950 | 0.0008 | - |
|
| 274 |
+
| 2.5253 | 4000 | 0.0005 | - |
|
| 275 |
+
| 2.5568 | 4050 | 0.0006 | - |
|
| 276 |
+
| 2.5884 | 4100 | 0.0011 | - |
|
| 277 |
+
| 2.6199 | 4150 | 0.0021 | - |
|
| 278 |
+
| 2.6515 | 4200 | 0.0001 | - |
|
| 279 |
+
| 2.6831 | 4250 | 0.0004 | - |
|
| 280 |
+
| 2.7146 | 4300 | 0.0002 | - |
|
| 281 |
+
| 2.7462 | 4350 | 0.0002 | - |
|
| 282 |
+
| 2.7778 | 4400 | 0.002 | - |
|
| 283 |
+
| 2.8093 | 4450 | 0.0007 | - |
|
| 284 |
+
| 2.8409 | 4500 | 0.0004 | - |
|
| 285 |
+
| 2.8725 | 4550 | 0.0001 | - |
|
| 286 |
+
| 2.9040 | 4600 | 0.0001 | - |
|
| 287 |
+
| 2.9356 | 4650 | 0.0005 | - |
|
| 288 |
+
| 2.9672 | 4700 | 0.0018 | - |
|
| 289 |
+
| 2.9987 | 4750 | 0.0001 | - |
|
| 290 |
+
| 3.0303 | 4800 | 0.0001 | - |
|
| 291 |
+
| 3.0619 | 4850 | 0.0001 | - |
|
| 292 |
+
| 3.0934 | 4900 | 0.0014 | - |
|
| 293 |
+
| 3.125 | 4950 | 0.0001 | - |
|
| 294 |
+
| 3.1566 | 5000 | 0.0001 | - |
|
| 295 |
+
| 3.1881 | 5050 | 0.0001 | - |
|
| 296 |
+
| 3.2197 | 5100 | 0.0002 | - |
|
| 297 |
+
| 3.2513 | 5150 | 0.0002 | - |
|
| 298 |
+
| 3.2828 | 5200 | 0.0 | - |
|
| 299 |
+
| 3.3144 | 5250 | 0.0001 | - |
|
| 300 |
+
| 3.3460 | 5300 | 0.0001 | - |
|
| 301 |
+
| 3.3775 | 5350 | 0.0 | - |
|
| 302 |
+
| 3.4091 | 5400 | 0.0001 | - |
|
| 303 |
+
| 3.4407 | 5450 | 0.0001 | - |
|
| 304 |
+
| 3.4722 | 5500 | 0.0009 | - |
|
| 305 |
+
| 3.5038 | 5550 | 0.0003 | - |
|
| 306 |
+
| 3.5354 | 5600 | 0.001 | - |
|
| 307 |
+
| 3.5669 | 5650 | 0.0021 | - |
|
| 308 |
+
| 3.5985 | 5700 | 0.0015 | - |
|
| 309 |
+
| 3.6301 | 5750 | 0.0001 | - |
|
| 310 |
+
| 3.6616 | 5800 | 0.0001 | - |
|
| 311 |
+
| 3.6932 | 5850 | 0.0001 | - |
|
| 312 |
+
| 3.7247 | 5900 | 0.0001 | - |
|
| 313 |
+
| 3.7563 | 5950 | 0.0001 | - |
|
| 314 |
+
| 3.7879 | 6000 | 0.0001 | - |
|
| 315 |
+
| 3.8194 | 6050 | 0.0001 | - |
|
| 316 |
+
| 3.8510 | 6100 | 0.0001 | - |
|
| 317 |
+
| 3.8826 | 6150 | 0.0001 | - |
|
| 318 |
+
| 3.9141 | 6200 | 0.0 | - |
|
| 319 |
+
| 3.9457 | 6250 | 0.0001 | - |
|
| 320 |
+
| 3.9773 | 6300 | 0.0012 | - |
|
| 321 |
+
| 4.0088 | 6350 | 0.0001 | - |
|
| 322 |
+
| 4.0404 | 6400 | 0.0005 | - |
|
| 323 |
+
| 4.0720 | 6450 | 0.0001 | - |
|
| 324 |
+
| 4.1035 | 6500 | 0.0001 | - |
|
| 325 |
+
| 4.1351 | 6550 | 0.0001 | - |
|
| 326 |
+
| 4.1667 | 6600 | 0.0001 | - |
|
| 327 |
+
| 4.1982 | 6650 | 0.0 | - |
|
| 328 |
+
| 4.2298 | 6700 | 0.0001 | - |
|
| 329 |
+
| 4.2614 | 6750 | 0.0003 | - |
|
| 330 |
+
| 4.2929 | 6800 | 0.0001 | - |
|
| 331 |
+
| 4.3245 | 6850 | 0.0007 | - |
|
| 332 |
+
| 4.3561 | 6900 | 0.0002 | - |
|
| 333 |
+
| 4.3876 | 6950 | 0.0001 | - |
|
| 334 |
+
| 4.4192 | 7000 | 0.0004 | - |
|
| 335 |
+
| 4.4508 | 7050 | 0.0001 | - |
|
| 336 |
+
| 4.4823 | 7100 | 0.0002 | - |
|
| 337 |
+
| 4.5139 | 7150 | 0.0001 | - |
|
| 338 |
+
| 4.5455 | 7200 | 0.0 | - |
|
| 339 |
+
| 4.5770 | 7250 | 0.0 | - |
|
| 340 |
+
| 4.6086 | 7300 | 0.0 | - |
|
| 341 |
+
| 4.6402 | 7350 | 0.0 | - |
|
| 342 |
+
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| 343 |
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| 344 |
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| 542 |
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| 550 |
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| 553 |
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| 554 |
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| 555 |
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| 556 |
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| 558 |
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| 563 |
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| 564 |
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| 566 |
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| 571 |
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| 572 |
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| 612 |
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| 619 |
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| 621 |
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| 622 |
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| 623 |
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| 626 |
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| 627 |
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| 629 |
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| 631 |
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| 632 |
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| 633 |
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| 13.8573 | 21950 | 0.0 | - |
|
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+
| 13.8889 | 22000 | 0.0 | - |
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+
| 13.9205 | 22050 | 0.0 | - |
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+
| 13.9520 | 22100 | 0.0 | - |
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| 13.9836 | 22150 | 0.0 | - |
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| 14.0152 | 22200 | 0.0 | - |
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+
| 14.0467 | 22250 | 0.0002 | - |
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+
| 14.0783 | 22300 | 0.0 | - |
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| 14.1098 | 22350 | 0.0 | - |
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| 14.2992 | 22650 | 0.0 | - |
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| 14.4886 | 22950 | 0.0 | - |
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| 14.5202 | 23000 | 0.0 | - |
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+
| 14.5518 | 23050 | 0.0 | - |
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| 14.6149 | 23150 | 0.0 | - |
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| 14.6780 | 23250 | 0.0 | - |
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| 14.7096 | 23300 | 0.0 | - |
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| 14.7412 | 23350 | 0.0 | - |
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| 14.8674 | 23550 | 0.0 | - |
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+
| 14.8990 | 23600 | 0.0 | - |
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+
| 14.9306 | 23650 | 0.0 | - |
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+
| 14.9621 | 23700 | 0.0 | - |
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+
| 14.9937 | 23750 | 0.0 | - |
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| 15.0253 | 23800 | 0.0 | - |
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| 15.0568 | 23850 | 0.0 | - |
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+
| 15.0884 | 23900 | 0.0 | - |
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+
| 15.1199 | 23950 | 0.0 | - |
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+
| 15.1515 | 24000 | 0.0 | - |
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+
| 15.1831 | 24050 | 0.0 | - |
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+
| 15.2146 | 24100 | 0.0 | - |
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| 15.2462 | 24150 | 0.0 | - |
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+
| 15.2778 | 24200 | 0.0 | - |
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| 15.3093 | 24250 | 0.0 | - |
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+
| 15.3409 | 24300 | 0.0 | - |
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+
| 15.3725 | 24350 | 0.0 | - |
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| 15.4040 | 24400 | 0.0 | - |
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| 15.4672 | 24500 | 0.0 | - |
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| 15.4987 | 24550 | 0.0 | - |
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| 15.5303 | 24600 | 0.0 | - |
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| 15.5619 | 24650 | 0.0 | - |
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+
| 15.5934 | 24700 | 0.0 | - |
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| 15.625 | 24750 | 0.0 | - |
|
| 690 |
+
| 15.6566 | 24800 | 0.0001 | - |
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+
| 15.6881 | 24850 | 0.0 | - |
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+
| 15.7197 | 24900 | 0.0 | - |
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| 15.7513 | 24950 | 0.0 | - |
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+
| 15.7828 | 25000 | 0.0 | - |
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| 15.8144 | 25050 | 0.0 | - |
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+
| 15.8460 | 25100 | 0.0 | - |
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| 15.8775 | 25150 | 0.0 | - |
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| 698 |
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| 15.9091 | 25200 | 0.0 | - |
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| 15.9407 | 25250 | 0.0 | - |
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| 700 |
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| 15.9722 | 25300 | 0.0 | - |
|
| 701 |
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| 16.0038 | 25350 | 0.0 | - |
|
| 702 |
+
| 16.0354 | 25400 | 0.0 | - |
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| 703 |
+
| 16.0669 | 25450 | 0.0 | - |
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| 704 |
+
| 16.0985 | 25500 | 0.0 | - |
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| 705 |
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| 16.1301 | 25550 | 0.0 | - |
|
| 706 |
+
| 16.1616 | 25600 | 0.0 | - |
|
| 707 |
+
| 16.1932 | 25650 | 0.0 | - |
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| 708 |
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| 16.2247 | 25700 | 0.0 | - |
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+
| 16.2563 | 25750 | 0.0 | - |
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| 16.2879 | 25800 | 0.0 | - |
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| 16.3194 | 25850 | 0.0 | - |
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| 16.3510 | 25900 | 0.0 | - |
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| 16.3826 | 25950 | 0.0 | - |
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| 16.4141 | 26000 | 0.0 | - |
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| 16.4457 | 26050 | 0.0 | - |
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| 16.4773 | 26100 | 0.0 | - |
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| 16.5088 | 26150 | 0.0 | - |
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| 16.5404 | 26200 | 0.0 | - |
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| 16.5720 | 26250 | 0.0 | - |
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| 16.6035 | 26300 | 0.0 | - |
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| 16.6351 | 26350 | 0.0 | - |
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| 16.6667 | 26400 | 0.0 | - |
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| 16.6982 | 26450 | 0.0 | - |
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| 16.7298 | 26500 | 0.0 | - |
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| 16.7614 | 26550 | 0.0 | - |
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| 16.7929 | 26600 | 0.0 | - |
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| 16.8245 | 26650 | 0.0 | - |
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| 16.8561 | 26700 | 0.0 | - |
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| 16.8876 | 26750 | 0.0 | - |
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+
| 16.9192 | 26800 | 0.0 | - |
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| 731 |
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| 16.9508 | 26850 | 0.0 | - |
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| 732 |
+
| 16.9823 | 26900 | 0.0 | - |
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| 733 |
+
| 17.0139 | 26950 | 0.0 | - |
|
| 734 |
+
| 17.0455 | 27000 | 0.0 | - |
|
| 735 |
+
| 17.0770 | 27050 | 0.0 | - |
|
| 736 |
+
| 17.1086 | 27100 | 0.0 | - |
|
| 737 |
+
| 17.1402 | 27150 | 0.0 | - |
|
| 738 |
+
| 17.1717 | 27200 | 0.0 | - |
|
| 739 |
+
| 17.2033 | 27250 | 0.0 | - |
|
| 740 |
+
| 17.2348 | 27300 | 0.0 | - |
|
| 741 |
+
| 17.2664 | 27350 | 0.0 | - |
|
| 742 |
+
| 17.2980 | 27400 | 0.0 | - |
|
| 743 |
+
| 17.3295 | 27450 | 0.0 | - |
|
| 744 |
+
| 17.3611 | 27500 | 0.0 | - |
|
| 745 |
+
| 17.3927 | 27550 | 0.0 | - |
|
| 746 |
+
| 17.4242 | 27600 | 0.0 | - |
|
| 747 |
+
| 17.4558 | 27650 | 0.0 | - |
|
| 748 |
+
| 17.4874 | 27700 | 0.0 | - |
|
| 749 |
+
| 17.5189 | 27750 | 0.0 | - |
|
| 750 |
+
| 17.5505 | 27800 | 0.0 | - |
|
| 751 |
+
| 17.5821 | 27850 | 0.0 | - |
|
| 752 |
+
| 17.6136 | 27900 | 0.0 | - |
|
| 753 |
+
| 17.6452 | 27950 | 0.0 | - |
|
| 754 |
+
| 17.6768 | 28000 | 0.0 | - |
|
| 755 |
+
| 17.7083 | 28050 | 0.0 | - |
|
| 756 |
+
| 17.7399 | 28100 | 0.0 | - |
|
| 757 |
+
| 17.7715 | 28150 | 0.0 | - |
|
| 758 |
+
| 17.8030 | 28200 | 0.0 | - |
|
| 759 |
+
| 17.8346 | 28250 | 0.0 | - |
|
| 760 |
+
| 17.8662 | 28300 | 0.0 | - |
|
| 761 |
+
| 17.8977 | 28350 | 0.0 | - |
|
| 762 |
+
| 17.9293 | 28400 | 0.0 | - |
|
| 763 |
+
| 17.9609 | 28450 | 0.0 | - |
|
| 764 |
+
| 17.9924 | 28500 | 0.0 | - |
|
| 765 |
+
| 18.0240 | 28550 | 0.0 | - |
|
| 766 |
+
| 18.0556 | 28600 | 0.0 | - |
|
| 767 |
+
| 18.0871 | 28650 | 0.0 | - |
|
| 768 |
+
| 18.1187 | 28700 | 0.0 | - |
|
| 769 |
+
| 18.1503 | 28750 | 0.0 | - |
|
| 770 |
+
| 18.1818 | 28800 | 0.0 | - |
|
| 771 |
+
| 18.2134 | 28850 | 0.0 | - |
|
| 772 |
+
| 18.2449 | 28900 | 0.0 | - |
|
| 773 |
+
| 18.2765 | 28950 | 0.0 | - |
|
| 774 |
+
| 18.3081 | 29000 | 0.0 | - |
|
| 775 |
+
| 18.3396 | 29050 | 0.0 | - |
|
| 776 |
+
| 18.3712 | 29100 | 0.0 | - |
|
| 777 |
+
| 18.4028 | 29150 | 0.0 | - |
|
| 778 |
+
| 18.4343 | 29200 | 0.0 | - |
|
| 779 |
+
| 18.4659 | 29250 | 0.0 | - |
|
| 780 |
+
| 18.4975 | 29300 | 0.0 | - |
|
| 781 |
+
| 18.5290 | 29350 | 0.0 | - |
|
| 782 |
+
| 18.5606 | 29400 | 0.0 | - |
|
| 783 |
+
| 18.5922 | 29450 | 0.0 | - |
|
| 784 |
+
| 18.6237 | 29500 | 0.0 | - |
|
| 785 |
+
| 18.6553 | 29550 | 0.0 | - |
|
| 786 |
+
| 18.6869 | 29600 | 0.0 | - |
|
| 787 |
+
| 18.7184 | 29650 | 0.0 | - |
|
| 788 |
+
| 18.75 | 29700 | 0.0 | - |
|
| 789 |
+
| 18.7816 | 29750 | 0.0 | - |
|
| 790 |
+
| 18.8131 | 29800 | 0.0 | - |
|
| 791 |
+
| 18.8447 | 29850 | 0.0 | - |
|
| 792 |
+
| 18.8763 | 29900 | 0.0 | - |
|
| 793 |
+
| 18.9078 | 29950 | 0.0 | - |
|
| 794 |
+
| 18.9394 | 30000 | 0.0 | - |
|
| 795 |
+
| 18.9710 | 30050 | 0.0 | - |
|
| 796 |
+
| 19.0025 | 30100 | 0.0 | - |
|
| 797 |
+
| 19.0341 | 30150 | 0.0 | - |
|
| 798 |
+
| 19.0657 | 30200 | 0.0 | - |
|
| 799 |
+
| 19.0972 | 30250 | 0.0 | - |
|
| 800 |
+
| 19.1288 | 30300 | 0.0 | - |
|
| 801 |
+
| 19.1604 | 30350 | 0.0 | - |
|
| 802 |
+
| 19.1919 | 30400 | 0.0 | - |
|
| 803 |
+
| 19.2235 | 30450 | 0.0 | - |
|
| 804 |
+
| 19.2551 | 30500 | 0.0 | - |
|
| 805 |
+
| 19.2866 | 30550 | 0.0 | - |
|
| 806 |
+
| 19.3182 | 30600 | 0.0 | - |
|
| 807 |
+
| 19.3497 | 30650 | 0.0 | - |
|
| 808 |
+
| 19.3813 | 30700 | 0.0 | - |
|
| 809 |
+
| 19.4129 | 30750 | 0.0 | - |
|
| 810 |
+
| 19.4444 | 30800 | 0.0 | - |
|
| 811 |
+
| 19.4760 | 30850 | 0.0 | - |
|
| 812 |
+
| 19.5076 | 30900 | 0.0 | - |
|
| 813 |
+
| 19.5391 | 30950 | 0.0 | - |
|
| 814 |
+
| 19.5707 | 31000 | 0.0 | - |
|
| 815 |
+
| 19.6023 | 31050 | 0.0 | - |
|
| 816 |
+
| 19.6338 | 31100 | 0.0 | - |
|
| 817 |
+
| 19.6654 | 31150 | 0.0 | - |
|
| 818 |
+
| 19.6970 | 31200 | 0.0 | - |
|
| 819 |
+
| 19.7285 | 31250 | 0.0 | - |
|
| 820 |
+
| 19.7601 | 31300 | 0.0 | - |
|
| 821 |
+
| 19.7917 | 31350 | 0.0 | - |
|
| 822 |
+
| 19.8232 | 31400 | 0.0 | - |
|
| 823 |
+
| 19.8548 | 31450 | 0.0 | - |
|
| 824 |
+
| 19.8864 | 31500 | 0.0 | - |
|
| 825 |
+
| 19.9179 | 31550 | 0.0 | - |
|
| 826 |
+
| 19.9495 | 31600 | 0.0 | - |
|
| 827 |
+
| 19.9811 | 31650 | 0.0 | - |
|
| 828 |
+
|
| 829 |
+
### Framework Versions
|
| 830 |
+
- Python: 3.10.12
|
| 831 |
+
- SetFit: 1.1.0.dev0
|
| 832 |
+
- Sentence Transformers: 3.1.1
|
| 833 |
+
- Transformers: 4.46.1
|
| 834 |
+
- PyTorch: 2.4.0+cu121
|
| 835 |
+
- Datasets: 2.20.0
|
| 836 |
+
- Tokenizers: 0.20.0
|
| 837 |
+
|
| 838 |
+
## Citation
|
| 839 |
+
|
| 840 |
+
### BibTeX
|
| 841 |
+
```bibtex
|
| 842 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 843 |
+
doi = {10.48550/ARXIV.2209.11055},
|
| 844 |
+
url = {https://arxiv.org/abs/2209.11055},
|
| 845 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 846 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 847 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
| 848 |
+
publisher = {arXiv},
|
| 849 |
+
year = {2022},
|
| 850 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
| 851 |
+
}
|
| 852 |
+
```
|
| 853 |
+
|
| 854 |
+
<!--
|
| 855 |
+
## Glossary
|
| 856 |
+
|
| 857 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 858 |
+
-->
|
| 859 |
+
|
| 860 |
+
<!--
|
| 861 |
+
## Model Card Authors
|
| 862 |
+
|
| 863 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 864 |
+
-->
|
| 865 |
+
|
| 866 |
+
<!--
|
| 867 |
+
## Model Card Contact
|
| 868 |
+
|
| 869 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 870 |
+
-->
|
config.json
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "mini1013/master_domain",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"RobertaModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"bos_token_id": 0,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"eos_token_id": 2,
|
| 10 |
+
"gradient_checkpointing": false,
|
| 11 |
+
"hidden_act": "gelu",
|
| 12 |
+
"hidden_dropout_prob": 0.1,
|
| 13 |
+
"hidden_size": 768,
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 3072,
|
| 16 |
+
"layer_norm_eps": 1e-05,
|
| 17 |
+
"max_position_embeddings": 514,
|
| 18 |
+
"model_type": "roberta",
|
| 19 |
+
"num_attention_heads": 12,
|
| 20 |
+
"num_hidden_layers": 12,
|
| 21 |
+
"pad_token_id": 1,
|
| 22 |
+
"position_embedding_type": "absolute",
|
| 23 |
+
"tokenizer_class": "BertTokenizer",
|
| 24 |
+
"torch_dtype": "float32",
|
| 25 |
+
"transformers_version": "4.46.1",
|
| 26 |
+
"type_vocab_size": 1,
|
| 27 |
+
"use_cache": true,
|
| 28 |
+
"vocab_size": 32000
|
| 29 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.1.1",
|
| 4 |
+
"transformers": "4.46.1",
|
| 5 |
+
"pytorch": "2.4.0+cu121"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": null
|
| 10 |
+
}
|
config_setfit.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"labels": null,
|
| 3 |
+
"normalize_embeddings": false
|
| 4 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:604d5ae61274a83a3ebd7e23c193d2e77b980850d50e891744f930ea80d4940f
|
| 3 |
+
size 442494816
|
model_head.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3f2c177052a1fb0cf93f058d2f5042211695a923a3c2a9cd21386469d348e43
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size 136327
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modules.json
ADDED
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@@ -0,0 +1,14 @@
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[
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{
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| 3 |
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"idx": 0,
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| 4 |
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"name": "0",
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| 5 |
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"path": "",
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| 6 |
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"type": "sentence_transformers.models.Transformer"
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| 7 |
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},
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| 8 |
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{
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| 9 |
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"idx": 1,
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| 10 |
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"name": "1",
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| 11 |
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"path": "1_Pooling",
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| 12 |
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"type": "sentence_transformers.models.Pooling"
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| 13 |
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}
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| 14 |
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]
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sentence_bert_config.json
ADDED
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{
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| 2 |
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"max_seq_length": 512,
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| 3 |
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"do_lower_case": false
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| 4 |
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}
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special_tokens_map.json
ADDED
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@@ -0,0 +1,51 @@
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| 1 |
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{
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| 2 |
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"bos_token": {
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| 3 |
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"content": "[CLS]",
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| 4 |
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"lstrip": false,
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| 5 |
+
"normalized": false,
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| 6 |
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"rstrip": false,
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| 7 |
+
"single_word": false
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| 8 |
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},
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| 9 |
+
"cls_token": {
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| 10 |
+
"content": "[CLS]",
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| 11 |
+
"lstrip": false,
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| 12 |
+
"normalized": false,
|
| 13 |
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"rstrip": false,
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| 14 |
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"single_word": false
|
| 15 |
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},
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| 16 |
+
"eos_token": {
|
| 17 |
+
"content": "[SEP]",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"mask_token": {
|
| 24 |
+
"content": "[MASK]",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"pad_token": {
|
| 31 |
+
"content": "[PAD]",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
},
|
| 37 |
+
"sep_token": {
|
| 38 |
+
"content": "[SEP]",
|
| 39 |
+
"lstrip": false,
|
| 40 |
+
"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
+
"single_word": false
|
| 43 |
+
},
|
| 44 |
+
"unk_token": {
|
| 45 |
+
"content": "[UNK]",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false
|
| 50 |
+
}
|
| 51 |
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}
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tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
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@@ -0,0 +1,66 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[CLS]",
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| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "[PAD]",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "[SEP]",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"3": {
|
| 28 |
+
"content": "[UNK]",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"4": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"bos_token": "[CLS]",
|
| 45 |
+
"clean_up_tokenization_spaces": false,
|
| 46 |
+
"cls_token": "[CLS]",
|
| 47 |
+
"do_basic_tokenize": true,
|
| 48 |
+
"do_lower_case": false,
|
| 49 |
+
"eos_token": "[SEP]",
|
| 50 |
+
"mask_token": "[MASK]",
|
| 51 |
+
"max_length": 512,
|
| 52 |
+
"model_max_length": 512,
|
| 53 |
+
"never_split": null,
|
| 54 |
+
"pad_to_multiple_of": null,
|
| 55 |
+
"pad_token": "[PAD]",
|
| 56 |
+
"pad_token_type_id": 0,
|
| 57 |
+
"padding_side": "right",
|
| 58 |
+
"sep_token": "[SEP]",
|
| 59 |
+
"stride": 0,
|
| 60 |
+
"strip_accents": null,
|
| 61 |
+
"tokenize_chinese_chars": true,
|
| 62 |
+
"tokenizer_class": "BertTokenizer",
|
| 63 |
+
"truncation_side": "right",
|
| 64 |
+
"truncation_strategy": "longest_first",
|
| 65 |
+
"unk_token": "[UNK]"
|
| 66 |
+
}
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vocab.txt
ADDED
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The diff for this file is too large to render.
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