Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +10 -0
- README.md +391 -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
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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
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| 1 |
+
---
|
| 2 |
+
base_model: mini1013/master_domain
|
| 3 |
+
library_name: setfit
|
| 4 |
+
metrics:
|
| 5 |
+
- accuracy
|
| 6 |
+
pipeline_tag: text-classification
|
| 7 |
+
tags:
|
| 8 |
+
- setfit
|
| 9 |
+
- sentence-transformers
|
| 10 |
+
- text-classification
|
| 11 |
+
- generated_from_setfit_trainer
|
| 12 |
+
widget:
|
| 13 |
+
- text: 1분완성 네일팁 모음인조손톱 인조팁 붙이는네일팁 웨딩네 13)샤인네일팁-화이트 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
|
| 14 |
+
LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
|
| 15 |
+
- text: 오피아이 인피니트샤인2 매니큐어 MI12 × 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일
|
| 16 |
+
> 일반네일 > 컬러 매니큐어
|
| 17 |
+
- text: 오피아이 젤 네일 컬러 GCV33 x 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일
|
| 18 |
+
> 컬러 매니큐어
|
| 19 |
+
- text: 디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트
|
| 20 |
+
- text: OPI 인피니트샤인 HRL31 LETS BE FRIENDS HRL31 - LETS BE FRIENDS! LotteOn > 뷰티 > 헤어/바디
|
| 21 |
+
> 헤어스타일링 > 염색/매니큐어 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어
|
| 22 |
+
inference: true
|
| 23 |
+
model-index:
|
| 24 |
+
- name: SetFit with mini1013/master_domain
|
| 25 |
+
results:
|
| 26 |
+
- task:
|
| 27 |
+
type: text-classification
|
| 28 |
+
name: Text Classification
|
| 29 |
+
dataset:
|
| 30 |
+
name: Unknown
|
| 31 |
+
type: unknown
|
| 32 |
+
split: test
|
| 33 |
+
metrics:
|
| 34 |
+
- type: accuracy
|
| 35 |
+
value: 0.5301810865191147
|
| 36 |
+
name: Accuracy
|
| 37 |
+
---
|
| 38 |
+
|
| 39 |
+
# SetFit with mini1013/master_domain
|
| 40 |
+
|
| 41 |
+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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.
|
| 42 |
+
|
| 43 |
+
The model has been trained using an efficient few-shot learning technique that involves:
|
| 44 |
+
|
| 45 |
+
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
|
| 46 |
+
2. Training a classification head with features from the fine-tuned Sentence Transformer.
|
| 47 |
+
|
| 48 |
+
## Model Details
|
| 49 |
+
|
| 50 |
+
### Model Description
|
| 51 |
+
- **Model Type:** SetFit
|
| 52 |
+
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
|
| 53 |
+
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
| 54 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 55 |
+
- **Number of Classes:** 4 classes
|
| 56 |
+
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
|
| 57 |
+
<!-- - **Language:** Unknown -->
|
| 58 |
+
<!-- - **License:** Unknown -->
|
| 59 |
+
|
| 60 |
+
### Model Sources
|
| 61 |
+
|
| 62 |
+
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
|
| 63 |
+
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
|
| 64 |
+
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
| 65 |
+
|
| 66 |
+
### Model Labels
|
| 67 |
+
| Label | Examples |
|
| 68 |
+
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 69 |
+
| 3 | <ul><li>'네일팁 실크익스텐션 311160L1720771597 티타늄금 물방울 （풀값 ） LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이 LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이'</li><li>'엔비베베 어린이 화장품 선물세트 어린이 썬쿠션+키즈네일스티커+워시패드 1개 (#M)쿠팡 홈>뷰티>어린이화장품>세트/키트 Coupang > 뷰티 > 어린이화장품 > 세트/키트'</li><li>'래쉬톡 원터치 인조 속눈썹 섹시 걸 × 3개입 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li></ul> |
|
| 70 |
+
| 0 | <ul><li>'오피아이 넌아세톤 리무버 빨강 30ml × 5개 (#M)쿠팡 홈>뷰티>네일>일반네일>리무버 Coupang > 뷰티 > 네일 > 일반네일 > 리무버'</li><li>'[OPI][리무버] 넌아세톤리무버 30ml ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일'</li><li>'포먼트 젤���일 O.4 블러쉬 뷰티 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'</li></ul> |
|
| 71 |
+
| 2 | <ul><li>'오피아이 프로스파 오일투고 큐티클 오일2197877 1 7.5ml x 1개2197877 1 (#M)SSG.COM/메이크업/베이스메이크업/컨실러 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 컨실러'</li><li>'구찌 뷰티 [구찌] 베르니 아 옹글 하이 샤인 네일 라커 712 멜린다 그린 × 선택완료 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일 > 컬러 매니큐어'</li><li>'OPI ProSpa 각질 제거 큐티클 크림, 27ml SSG.COM/메이크업/베이스메이크업/메이크업베이스;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스'</li></ul> |
|
| 72 |
+
| 1 | <ul><li>'르 베르니 루쥬 느와르 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어'</li><li>'베씨 베이스젤 + 탑젤 + 지브라파일 2p 세트 베이스젤, 탑젤, 지브라파일(100/150) × 1세트 LotteOn > 뷰티 > 네일 > 네일아트소품 LotteOn > 뷰티 > 네일 > 네일아트소품'</li><li>'OPI OPI Chrome Effects Nail Lacquer Top Coat CPT31 - 0.5 oz 상세내용참조 × 상세내용참조 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'</li></ul> |
|
| 73 |
+
|
| 74 |
+
## Evaluation
|
| 75 |
+
|
| 76 |
+
### Metrics
|
| 77 |
+
| Label | Accuracy |
|
| 78 |
+
|:--------|:---------|
|
| 79 |
+
| **all** | 0.5302 |
|
| 80 |
+
|
| 81 |
+
## Uses
|
| 82 |
+
|
| 83 |
+
### Direct Use for Inference
|
| 84 |
+
|
| 85 |
+
First install the SetFit library:
|
| 86 |
+
|
| 87 |
+
```bash
|
| 88 |
+
pip install setfit
|
| 89 |
+
```
|
| 90 |
+
|
| 91 |
+
Then you can load this model and run inference.
|
| 92 |
+
|
| 93 |
+
```python
|
| 94 |
+
from setfit import SetFitModel
|
| 95 |
+
|
| 96 |
+
# Download from the 🤗 Hub
|
| 97 |
+
model = SetFitModel.from_pretrained("mini1013/master_cate_bt1_test_flat_top_cate")
|
| 98 |
+
# Run inference
|
| 99 |
+
preds = model("디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트")
|
| 100 |
+
```
|
| 101 |
+
|
| 102 |
+
<!--
|
| 103 |
+
### Downstream Use
|
| 104 |
+
|
| 105 |
+
*List how someone could finetune this model on their own dataset.*
|
| 106 |
+
-->
|
| 107 |
+
|
| 108 |
+
<!--
|
| 109 |
+
### Out-of-Scope Use
|
| 110 |
+
|
| 111 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 112 |
+
-->
|
| 113 |
+
|
| 114 |
+
<!--
|
| 115 |
+
## Bias, Risks and Limitations
|
| 116 |
+
|
| 117 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 118 |
+
-->
|
| 119 |
+
|
| 120 |
+
<!--
|
| 121 |
+
### Recommendations
|
| 122 |
+
|
| 123 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 124 |
+
-->
|
| 125 |
+
|
| 126 |
+
## Training Details
|
| 127 |
+
|
| 128 |
+
### Training Set Metrics
|
| 129 |
+
| Training set | Min | Median | Max |
|
| 130 |
+
|:-------------|:----|:--------|:----|
|
| 131 |
+
| Word count | 13 | 22.7236 | 41 |
|
| 132 |
+
|
| 133 |
+
| Label | Training Sample Count |
|
| 134 |
+
|:------|:----------------------|
|
| 135 |
+
| 0 | 49 |
|
| 136 |
+
| 1 | 50 |
|
| 137 |
+
| 2 | 50 |
|
| 138 |
+
| 3 | 50 |
|
| 139 |
+
|
| 140 |
+
### Training Hyperparameters
|
| 141 |
+
- batch_size: (64, 64)
|
| 142 |
+
- num_epochs: (30, 30)
|
| 143 |
+
- max_steps: -1
|
| 144 |
+
- sampling_strategy: oversampling
|
| 145 |
+
- num_iterations: 100
|
| 146 |
+
- body_learning_rate: (2e-05, 1e-05)
|
| 147 |
+
- head_learning_rate: 0.01
|
| 148 |
+
- loss: CosineSimilarityLoss
|
| 149 |
+
- distance_metric: cosine_distance
|
| 150 |
+
- margin: 0.25
|
| 151 |
+
- end_to_end: False
|
| 152 |
+
- use_amp: False
|
| 153 |
+
- warmup_proportion: 0.1
|
| 154 |
+
- l2_weight: 0.01
|
| 155 |
+
- seed: 42
|
| 156 |
+
- eval_max_steps: -1
|
| 157 |
+
- load_best_model_at_end: False
|
| 158 |
+
|
| 159 |
+
### Training Results
|
| 160 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
| 161 |
+
|:-------:|:----:|:-------------:|:---------------:|
|
| 162 |
+
| 0.0032 | 1 | 0.4603 | - |
|
| 163 |
+
| 0.1608 | 50 | 0.4502 | - |
|
| 164 |
+
| 0.3215 | 100 | 0.4315 | - |
|
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+
| 0.4823 | 150 | 0.3996 | - |
|
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| 0.6431 | 200 | 0.365 | - |
|
| 167 |
+
| 0.8039 | 250 | 0.2954 | - |
|
| 168 |
+
| 0.9646 | 300 | 0.2647 | - |
|
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| 1.1254 | 350 | 0.2378 | - |
|
| 170 |
+
| 1.2862 | 400 | 0.2257 | - |
|
| 171 |
+
| 1.4469 | 450 | 0.2165 | - |
|
| 172 |
+
| 1.6077 | 500 | 0.213 | - |
|
| 173 |
+
| 1.7685 | 550 | 0.1999 | - |
|
| 174 |
+
| 1.9293 | 600 | 0.1838 | - |
|
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+
| 2.0900 | 650 | 0.1614 | - |
|
| 176 |
+
| 2.2508 | 700 | 0.1164 | - |
|
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+
| 2.4116 | 750 | 0.0553 | - |
|
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+
| 2.5723 | 800 | 0.0366 | - |
|
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+
| 2.7331 | 850 | 0.0279 | - |
|
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+
| 2.8939 | 900 | 0.0219 | - |
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| 3.0547 | 950 | 0.0166 | - |
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| 3.2154 | 1000 | 0.0111 | - |
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+
| 3.3762 | 1050 | 0.0067 | - |
|
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| 3.5370 | 1100 | 0.0084 | - |
|
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+
| 3.6977 | 1150 | 0.0066 | - |
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+
| 3.8585 | 1200 | 0.0048 | - |
|
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+
| 4.0193 | 1250 | 0.0028 | - |
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+
| 4.1801 | 1300 | 0.0005 | - |
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| 189 |
+
| 4.3408 | 1350 | 0.0003 | - |
|
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+
| 4.5016 | 1400 | 0.0004 | - |
|
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+
| 4.6624 | 1450 | 0.0001 | - |
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+
| 4.8232 | 1500 | 0.0001 | - |
|
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+
| 4.9839 | 1550 | 0.0001 | - |
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+
| 5.1447 | 1600 | 0.0001 | - |
|
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+
| 5.3055 | 1650 | 0.0001 | - |
|
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| 5.4662 | 1700 | 0.0002 | - |
|
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+
| 5.6270 | 1750 | 0.0 | - |
|
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+
| 5.7878 | 1800 | 0.0 | - |
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+
| 5.9486 | 1850 | 0.0 | - |
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+
| 6.1093 | 1900 | 0.0001 | - |
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+
| 6.2701 | 1950 | 0.0 | - |
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+
| 6.4309 | 2000 | 0.0 | - |
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+
| 6.5916 | 2050 | 0.0 | - |
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+
| 6.7524 | 2100 | 0.0 | - |
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+
| 6.9132 | 2150 | 0.0002 | - |
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+
| 7.0740 | 2200 | 0.0002 | - |
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+
| 7.2347 | 2250 | 0.0 | - |
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| 7.3955 | 2300 | 0.0 | - |
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| 7.5563 | 2350 | 0.0 | - |
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| 7.7170 | 2400 | 0.0 | - |
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| 7.8778 | 2450 | 0.0 | - |
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+
| 8.0386 | 2500 | 0.0 | - |
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+
| 8.1994 | 2550 | 0.0 | - |
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+
| 8.3601 | 2600 | 0.0 | - |
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+
| 8.5209 | 2650 | 0.0 | - |
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| 8.6817 | 2700 | 0.0 | - |
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+
| 8.8424 | 2750 | 0.0 | - |
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| 9.0032 | 2800 | 0.0 | - |
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| 9.1640 | 2850 | 0.0 | - |
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| 9.3248 | 2900 | 0.0 | - |
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| 9.4855 | 2950 | 0.0 | - |
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| 9.6463 | 3000 | 0.0 | - |
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| 9.8071 | 3050 | 0.0 | - |
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| 9.9678 | 3100 | 0.0 | - |
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| 10.1286 | 3150 | 0.0 | - |
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| 10.2894 | 3200 | 0.0 | - |
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| 10.4502 | 3250 | 0.0 | - |
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| 10.6109 | 3300 | 0.0 | - |
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| 10.7717 | 3350 | 0.0 | - |
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| 10.9325 | 3400 | 0.0 | - |
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| 11.0932 | 3450 | 0.0 | - |
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| 11.2540 | 3500 | 0.0 | - |
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| 11.4148 | 3550 | 0.0 | - |
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| 11.5756 | 3600 | 0.0 | - |
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+
| 11.7363 | 3650 | 0.0 | - |
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| 11.8971 | 3700 | 0.0 | - |
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+
| 12.0579 | 3750 | 0.0004 | - |
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| 12.2186 | 3800 | 0.0 | - |
|
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| 12.3794 | 3850 | 0.0001 | - |
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+
| 12.5402 | 3900 | 0.0001 | - |
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| 12.7010 | 3950 | 0.0 | - |
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+
| 12.8617 | 4000 | 0.0001 | - |
|
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+
| 13.0225 | 4050 | 0.0002 | - |
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+
| 13.1833 | 4100 | 0.0009 | - |
|
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+
| 13.3441 | 4150 | 0.0037 | - |
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+
| 13.5048 | 4200 | 0.0025 | - |
|
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+
| 13.6656 | 4250 | 0.0009 | - |
|
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+
| 13.8264 | 4300 | 0.0002 | - |
|
| 249 |
+
| 13.9871 | 4350 | 0.0002 | - |
|
| 250 |
+
| 14.1479 | 4400 | 0.0 | - |
|
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+
| 14.3087 | 4450 | 0.0002 | - |
|
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+
| 14.4695 | 4500 | 0.0001 | - |
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+
| 14.6302 | 4550 | 0.0004 | - |
|
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+
| 14.7910 | 4600 | 0.0008 | - |
|
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+
| 14.9518 | 4650 | 0.0 | - |
|
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+
| 15.1125 | 4700 | 0.0 | - |
|
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+
| 15.2733 | 4750 | 0.0001 | - |
|
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+
| 15.4341 | 4800 | 0.0 | - |
|
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+
| 15.5949 | 4850 | 0.0 | - |
|
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+
| 15.7556 | 4900 | 0.0002 | - |
|
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+
| 15.9164 | 4950 | 0.0 | - |
|
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+
| 16.0772 | 5000 | 0.0 | - |
|
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+
| 16.2379 | 5050 | 0.0001 | - |
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+
| 16.3987 | 5100 | 0.0 | - |
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| 16.5595 | 5150 | 0.0 | - |
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| 16.7203 | 5200 | 0.0 | - |
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+
| 16.8810 | 5250 | 0.0 | - |
|
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+
| 17.0418 | 5300 | 0.0 | - |
|
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+
| 17.2026 | 5350 | 0.0 | - |
|
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+
| 17.3633 | 5400 | 0.0 | - |
|
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+
| 17.5241 | 5450 | 0.0 | - |
|
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+
| 17.6849 | 5500 | 0.0 | - |
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+
| 17.8457 | 5550 | 0.0 | - |
|
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+
| 18.0064 | 5600 | 0.0 | - |
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+
| 18.1672 | 5650 | 0.0 | - |
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+
| 18.3280 | 5700 | 0.0 | - |
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| 18.4887 | 5750 | 0.0 | - |
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+
| 18.6495 | 5800 | 0.0 | - |
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| 18.8103 | 5850 | 0.0 | - |
|
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+
| 18.9711 | 5900 | 0.0 | - |
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| 19.1318 | 5950 | 0.0 | - |
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+
| 19.2926 | 6000 | 0.0 | - |
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| 19.4534 | 6050 | 0.0 | - |
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+
| 19.6141 | 6100 | 0.0 | - |
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+
| 19.7749 | 6150 | 0.0 | - |
|
| 286 |
+
| 19.9357 | 6200 | 0.0 | - |
|
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+
| 20.0965 | 6250 | 0.0 | - |
|
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+
| 20.2572 | 6300 | 0.0 | - |
|
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+
| 20.4180 | 6350 | 0.0 | - |
|
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+
| 20.5788 | 6400 | 0.0 | - |
|
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+
| 20.7395 | 6450 | 0.0 | - |
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| 20.9003 | 6500 | 0.0 | - |
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+
| 21.0611 | 6550 | 0.0 | - |
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+
| 21.2219 | 6600 | 0.0 | - |
|
| 295 |
+
| 21.3826 | 6650 | 0.0 | - |
|
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+
| 21.5434 | 6700 | 0.0 | - |
|
| 297 |
+
| 21.7042 | 6750 | 0.0 | - |
|
| 298 |
+
| 21.8650 | 6800 | 0.0 | - |
|
| 299 |
+
| 22.0257 | 6850 | 0.0 | - |
|
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+
| 22.1865 | 6900 | 0.0 | - |
|
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+
| 22.3473 | 6950 | 0.0 | - |
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+
| 22.5080 | 7000 | 0.0 | - |
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+
| 22.6688 | 7050 | 0.0 | - |
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| 304 |
+
| 22.8296 | 7100 | 0.0 | - |
|
| 305 |
+
| 22.9904 | 7150 | 0.0 | - |
|
| 306 |
+
| 23.1511 | 7200 | 0.0 | - |
|
| 307 |
+
| 23.3119 | 7250 | 0.0 | - |
|
| 308 |
+
| 23.4727 | 7300 | 0.0 | - |
|
| 309 |
+
| 23.6334 | 7350 | 0.0 | - |
|
| 310 |
+
| 23.7942 | 7400 | 0.0 | - |
|
| 311 |
+
| 23.9550 | 7450 | 0.0 | - |
|
| 312 |
+
| 24.1158 | 7500 | 0.0 | - |
|
| 313 |
+
| 24.2765 | 7550 | 0.0 | - |
|
| 314 |
+
| 24.4373 | 7600 | 0.0 | - |
|
| 315 |
+
| 24.5981 | 7650 | 0.0 | - |
|
| 316 |
+
| 24.7588 | 7700 | 0.0 | - |
|
| 317 |
+
| 24.9196 | 7750 | 0.0 | - |
|
| 318 |
+
| 25.0804 | 7800 | 0.0 | - |
|
| 319 |
+
| 25.2412 | 7850 | 0.0 | - |
|
| 320 |
+
| 25.4019 | 7900 | 0.0 | - |
|
| 321 |
+
| 25.5627 | 7950 | 0.0 | - |
|
| 322 |
+
| 25.7235 | 8000 | 0.0 | - |
|
| 323 |
+
| 25.8842 | 8050 | 0.0 | - |
|
| 324 |
+
| 26.0450 | 8100 | 0.0 | - |
|
| 325 |
+
| 26.2058 | 8150 | 0.0 | - |
|
| 326 |
+
| 26.3666 | 8200 | 0.0 | - |
|
| 327 |
+
| 26.5273 | 8250 | 0.0 | - |
|
| 328 |
+
| 26.6881 | 8300 | 0.0 | - |
|
| 329 |
+
| 26.8489 | 8350 | 0.0 | - |
|
| 330 |
+
| 27.0096 | 8400 | 0.0 | - |
|
| 331 |
+
| 27.1704 | 8450 | 0.0 | - |
|
| 332 |
+
| 27.3312 | 8500 | 0.0 | - |
|
| 333 |
+
| 27.4920 | 8550 | 0.0 | - |
|
| 334 |
+
| 27.6527 | 8600 | 0.0 | - |
|
| 335 |
+
| 27.8135 | 8650 | 0.0 | - |
|
| 336 |
+
| 27.9743 | 8700 | 0.0 | - |
|
| 337 |
+
| 28.1350 | 8750 | 0.0 | - |
|
| 338 |
+
| 28.2958 | 8800 | 0.0 | - |
|
| 339 |
+
| 28.4566 | 8850 | 0.0 | - |
|
| 340 |
+
| 28.6174 | 8900 | 0.0 | - |
|
| 341 |
+
| 28.7781 | 8950 | 0.0 | - |
|
| 342 |
+
| 28.9389 | 9000 | 0.0 | - |
|
| 343 |
+
| 29.0997 | 9050 | 0.0 | - |
|
| 344 |
+
| 29.2605 | 9100 | 0.0 | - |
|
| 345 |
+
| 29.4212 | 9150 | 0.0 | - |
|
| 346 |
+
| 29.5820 | 9200 | 0.0 | - |
|
| 347 |
+
| 29.7428 | 9250 | 0.0 | - |
|
| 348 |
+
| 29.9035 | 9300 | 0.0 | - |
|
| 349 |
+
|
| 350 |
+
### Framework Versions
|
| 351 |
+
- Python: 3.10.12
|
| 352 |
+
- SetFit: 1.1.0
|
| 353 |
+
- Sentence Transformers: 3.3.1
|
| 354 |
+
- Transformers: 4.44.2
|
| 355 |
+
- PyTorch: 2.2.0a0+81ea7a4
|
| 356 |
+
- Datasets: 3.2.0
|
| 357 |
+
- Tokenizers: 0.19.1
|
| 358 |
+
|
| 359 |
+
## Citation
|
| 360 |
+
|
| 361 |
+
### BibTeX
|
| 362 |
+
```bibtex
|
| 363 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 364 |
+
doi = {10.48550/ARXIV.2209.11055},
|
| 365 |
+
url = {https://arxiv.org/abs/2209.11055},
|
| 366 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 367 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 368 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
| 369 |
+
publisher = {arXiv},
|
| 370 |
+
year = {2022},
|
| 371 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
| 372 |
+
}
|
| 373 |
+
```
|
| 374 |
+
|
| 375 |
+
<!--
|
| 376 |
+
## Glossary
|
| 377 |
+
|
| 378 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 379 |
+
-->
|
| 380 |
+
|
| 381 |
+
<!--
|
| 382 |
+
## Model Card Authors
|
| 383 |
+
|
| 384 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 385 |
+
-->
|
| 386 |
+
|
| 387 |
+
<!--
|
| 388 |
+
## Model Card Contact
|
| 389 |
+
|
| 390 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 391 |
+
-->
|
config.json
ADDED
|
@@ -0,0 +1,29 @@
|
|
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|
|
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|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "mini1013/master_item_bt_test_flat_top",
|
| 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": "4.44.2",
|
| 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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|
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|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.3.1",
|
| 4 |
+
"transformers": "4.44.2",
|
| 5 |
+
"pytorch": "2.2.0a0+81ea7a4"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": "cosine"
|
| 10 |
+
}
|
config_setfit.json
ADDED
|
@@ -0,0 +1,4 @@
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|
|
|
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|
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|
|
|
|
|
| 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:e69a0cf17b4805ddac4b4e06ae151608db73544a5f28b81170153478e6655f18
|
| 3 |
+
size 442494816
|
model_head.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0950ed280535dc1d3c27fdbd3dce74147eaa5535e9246ab9784c019aac22190d
|
| 3 |
+
size 25479
|
modules.json
ADDED
|
@@ -0,0 +1,14 @@
|
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|
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|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
}
|
| 14 |
+
]
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"max_seq_length": 512,
|
| 3 |
+
"do_lower_case": false
|
| 4 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "[CLS]",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"cls_token": {
|
| 10 |
+
"content": "[CLS]",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 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 |
+
}
|
tokenizer.json
ADDED
|
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|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[CLS]",
|
| 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 |
+
}
|
vocab.txt
ADDED
|
The diff for this file is too large to render.
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|
|
|