Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,295 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 2pcs 커튼 간단한 컬러 자카드 반투명 화면 침실 거실 장식 가구/인테리어>커튼/블라인드>실커튼
+- text: 거실 커튼고정끈 침실 커텐정리줄 묶는끈 자석정리끈 가구/인테리어>커튼/블라인드>커튼액세서리
+- text: 이케아 RACKA 레카 커튼봉 부자재 봉 부품 가구/인테리어>커튼/블라인드>커튼>커튼링/봉
+- text: 버티컬 블라인드 커텐 암막 폭 버티컬 블라인드 클립 6개 DIY철물 가구/인테리어>커튼/블라인드>버티컬
+- text: 집 음식점 현관 나무 비즈발 문발 입구 장식 가구/인테리어>커튼/블라인드>비즈발
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: mini1013/master_domain
+model-index:
+- name: SetFit with mini1013/master_domain
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# SetFit with mini1013/master_domain
+
+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.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 10 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                               |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 9.0   | <ul><li>'누베스 베이직 콤비블라인드 에블린쉐이드 50x50 가구/인테리어>커튼/블라인드>콤비블라인드'</li><li>'창안애 내츄럴 콤비블라인드 커튼 거실 안방 30 x 30 가구/인테리어>커튼/블라인드>콤비블라인드'</li><li>'누베스 베이직 콤비 블라인드 에블린쉐이드 145 X 240 가구/인테리어>커튼/블라인드>콤비블라인드'</li></ul>              |
+| 6.0   | <ul><li>'탈의실 간이피팅룸 이동 접이식 야외 도어 커튼 휴대용 I자커튼 사무실 학교 13컬러 가구/인테리어>커튼/블라인드>자바라'</li><li>'자바라 도어 베란다 슬라이딩 칸막이 문 가구/인테리어>커튼/블라인드>자바라'</li><li>'접이식 도어 주방 화장실 방수 미닫이 문 자바라 커튼 가구/인테리어>커튼/블라인드>자바라'</li></ul>                 |
+| 5.0   | <ul><li>'창안애 허니콤블라인드 벌집쉐이드 90 x 150 2개 가구/인테리어>커튼/블라인드>블라인드'</li><li>'홈안애 오동나무 베란다 원목 우드블라인드 40 x 40 가구/인테리어>커튼/블라인드>블라인드'</li><li>'창안애 50mm 타공 알루미늄 블라인드 방염방수 30 x 30 가구/인테리어>커튼/블라인드>블라인드'</li></ul>                 |
+| 2.0   | <ul><li>'창안애 시그니처 클로브 암막롤스크린 30x30 가구/인테리어>커튼/블라인드>롤스크린'</li><li>'해비체 썬스크린 롤스크린 거실 베란다 창문 50x50 가구/인테리어>커튼/블라인드>롤스크린'</li><li>'아라크네 1+1 암막 롤스크린 소형 90 x 210 2개 가구/인테리어>커튼/블라인드>롤스크린'</li></ul>                         |
+| 8.0   | <ul><li>'기숙사 가림막 학생방 이층침대커튼 암막 프라이버시 가구/인테리어>커튼/블라인드>커튼/로만세트'</li><li>'메종드모노 메이체크 먼지없는 가리개커튼 창문형긴창형 창문형 135x150 1장 가구/인테리어>커튼/블라인드>커튼/로만세트'</li><li>'폭커튼 커튼링 가구/인테리어>커튼/블라인드>커튼>커튼링/봉'</li></ul>                       |
+| 3.0   | <ul><li>'창문가리개 크기 가구/인테리어>커튼/블라인드>바란스'</li><li>'실커튼 발 가림막 인테리어 줄커튼 가리개 가구/인테리어>커튼/블라인드>바란스'</li><li>'쥬앤크홈 멜랑 체크 가리개커튼 7색상 맞춤 가림막커튼 패브릭 창문 베란다 현관 중문 선반 가구/인테리어>커튼/블라인드>바란스'</li></ul>                                  |
+| 1.0   | <ul><li>'부드러운 문짝 커튼 내마모성 파티션 호텔 가구/인테리어>커튼/블라인드>로만셰이드'</li><li>'클래식 시즌 커튼 침실 장식 뜨개질 대만 풍수 가구/인테리어>커튼/블라인드>로만셰이드'</li><li>'로만쉐이드 나비 미국 축구 선수 커튼 욕실 3D 로먼쉐이드 가구/인테리어>커튼/블라인드>로만셰이드'</li></ul>                            |
+| 4.0   | <ul><li>'롤스크린버티컬 블라인드버티컬 고정클립 카페 커피숍 6개 PW826DA4 가구/인테리어>커튼/블라인드>버티컬'</li><li>'암막 롤 스크린 러 바이저 휴대용 셔터 자동차 앞 유리 흡입 컵 주방 사무용품 7 - 가구/인테리어>커튼/블라인드>버티컬'</li><li>'햇빛가리개 붙이는 롤업 블라인드 58x125 가구/인테리어>커튼/블라인드>버티컬'</li></ul>   |
+| 0.0   | <ul><li>'커튼핀 콤비손잡이 세련된 꽃 모양 창 커튼 1 개 백 리본 커튼장식 롤스크린부품 가구/인테리어>커튼/블라인드>커튼액세서리'</li><li>'백장미 플라워 커튼 집게 1P 가구/인테리어>커튼/블라인드>커튼액세서리'</li><li>'타이백 커튼 고정끈 커텐핀 커튼집게 커텐걸이 가구/인테리어>커튼/블라인드>커튼액세서리'</li></ul>                    |
+| 7.0   | <ul><li>'Jon 가든 출입문 모기장 현관방충망 가구/인테리어>커튼/블라인드>캐노피'</li><li>'이층침대모기장 낮은 천장형 모기장 2층 침대 커튼 슈퍼 싱글 베드 커텐형 천막 가구/인테리어>커튼/블라인드>캐노피'</li><li>'사무실햇빛가리개 책상 모니터 비침 형광등 자유조정 사무실 침대 캐노피 가림막 다용도 커버 가구/인테리어>커튼/블라인드>캐노피'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("mini1013/master_cate_fi15")
+# Run inference
+preds = model("집 음식점 현관 나무 비즈발 문발 입구 장식 가구/인테리어>커튼/블라인드>비즈발")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 2   | 9.1343 | 20  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 5.0   | 70                    |
+| 6.0   | 70                    |
+| 7.0   | 70                    |
+| 8.0   | 70                    |
+| 9.0   | 70                    |
+
+### Training Hyperparameters
+- batch_size: (256, 256)
+- num_epochs: (30, 30)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 50
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0073  | 1    | 0.4829        | -               |
+| 0.3650  | 50   | 0.4991        | -               |
+| 0.7299  | 100  | 0.4963        | -               |
+| 1.0949  | 150  | 0.3354        | -               |
+| 1.4599  | 200  | 0.0958        | -               |
+| 1.8248  | 250  | 0.0204        | -               |
+| 2.1898  | 300  | 0.0065        | -               |
+| 2.5547  | 350  | 0.0004        | -               |
+| 2.9197  | 400  | 0.0002        | -               |
+| 3.2847  | 450  | 0.0002        | -               |
+| 3.6496  | 500  | 0.0001        | -               |
+| 4.0146  | 550  | 0.0001        | -               |
+| 4.3796  | 600  | 0.0001        | -               |
+| 4.7445  | 650  | 0.0001        | -               |
+| 5.1095  | 700  | 0.0001        | -               |
+| 5.4745  | 750  | 0.0001        | -               |
+| 5.8394  | 800  | 0.0001        | -               |
+| 6.2044  | 850  | 0.0001        | -               |
+| 6.5693  | 900  | 0.0001        | -               |
+| 6.9343  | 950  | 0.0001        | -               |
+| 7.2993  | 1000 | 0.0           | -               |
+| 7.6642  | 1050 | 0.0           | -               |
+| 8.0292  | 1100 | 0.0           | -               |
+| 8.3942  | 1150 | 0.0           | -               |
+| 8.7591  | 1200 | 0.0           | -               |
+| 9.1241  | 1250 | 0.0           | -               |
+| 9.4891  | 1300 | 0.0           | -               |
+| 9.8540  | 1350 | 0.0           | -               |
+| 10.2190 | 1400 | 0.0           | -               |
+| 10.5839 | 1450 | 0.0           | -               |
+| 10.9489 | 1500 | 0.0           | -               |
+| 11.3139 | 1550 | 0.0           | -               |
+| 11.6788 | 1600 | 0.0           | -               |
+| 12.0438 | 1650 | 0.0           | -               |
+| 12.4088 | 1700 | 0.0           | -               |
+| 12.7737 | 1750 | 0.0           | -               |
+| 13.1387 | 1800 | 0.0           | -               |
+| 13.5036 | 1850 | 0.0           | -               |
+| 13.8686 | 1900 | 0.0           | -               |
+| 14.2336 | 1950 | 0.0           | -               |
+| 14.5985 | 2000 | 0.0           | -               |
+| 14.9635 | 2050 | 0.0           | -               |
+| 15.3285 | 2100 | 0.0           | -               |
+| 15.6934 | 2150 | 0.0           | -               |
+| 16.0584 | 2200 | 0.0           | -               |
+| 16.4234 | 2250 | 0.0           | -               |
+| 16.7883 | 2300 | 0.0           | -               |
+| 17.1533 | 2350 | 0.0           | -               |
+| 17.5182 | 2400 | 0.0           | -               |
+| 17.8832 | 2450 | 0.0           | -               |
+| 18.2482 | 2500 | 0.0           | -               |
+| 18.6131 | 2550 | 0.0           | -               |
+| 18.9781 | 2600 | 0.0           | -               |
+| 19.3431 | 2650 | 0.0           | -               |
+| 19.7080 | 2700 | 0.0           | -               |
+| 20.0730 | 2750 | 0.0           | -               |
+| 20.4380 | 2800 | 0.0           | -               |
+| 20.8029 | 2850 | 0.0           | -               |
+| 21.1679 | 2900 | 0.0           | -               |
+| 21.5328 | 2950 | 0.0           | -               |
+| 21.8978 | 3000 | 0.0           | -               |
+| 22.2628 | 3050 | 0.0           | -               |
+| 22.6277 | 3100 | 0.0           | -               |
+| 22.9927 | 3150 | 0.0           | -               |
+| 23.3577 | 3200 | 0.0           | -               |
+| 23.7226 | 3250 | 0.0           | -               |
+| 24.0876 | 3300 | 0.0           | -               |
+| 24.4526 | 3350 | 0.0           | -               |
+| 24.8175 | 3400 | 0.0           | -               |
+| 25.1825 | 3450 | 0.0           | -               |
+| 25.5474 | 3500 | 0.0           | -               |
+| 25.9124 | 3550 | 0.0           | -               |
+| 26.2774 | 3600 | 0.0           | -               |
+| 26.6423 | 3650 | 0.0           | -               |
+| 27.0073 | 3700 | 0.0           | -               |
+| 27.3723 | 3750 | 0.0           | -               |
+| 27.7372 | 3800 | 0.0           | -               |
+| 28.1022 | 3850 | 0.0           | -               |
+| 28.4672 | 3900 | 0.0           | -               |
+| 28.8321 | 3950 | 0.0           | -               |
+| 29.1971 | 4000 | 0.0           | -               |
+| 29.5620 | 4050 | 0.0           | -               |
+| 29.9270 | 4100 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.44.2
+- PyTorch: 2.2.0a0+81ea7a4
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/master_item_fi",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.2.0a0+81ea7a4"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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model_head.pkl

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modules.json

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sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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+}

tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}