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 +1,285 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 자클라이 웨스트바이킹 쿨 바라클라바 넥워머 여름 자외선차단 LF1360 스포츠/레저>스포츠액세서리>스포츠넥워머
+- text: 등산 자전거 바이크 멀티 스카프 스포츠/레저>스포츠액세서리>아이스머플러/스카프
+- text: 축구작전판 축구전술판 스코어보드 팀명로고인쇄 스포츠/레저>스포츠액세서리>스코어보드/작전판
+- text: 등산 낚시 동계 필수 아웃도어 전체 기모 방한 마스크 스포츠/레저>스포츠액세서리>스포츠마스크
+- text: 전자점수판 농구전광판 디지털점수판 세자리 배드민턴 N2 스포츠/레저>스포츠액세서리>스코어보드/작전판
+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: 0.9989235737351991
+      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:** 9 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                                                                                                                                                                                                                                                               |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1.0   | <ul><li>'고급형 ABS PP 점수판 가방포함 5세트 30점 중형 44x24 체육용 스포츠/레저>스포츠액세서리>스코어보드/작전판'</li><li>'폭스40 클립보드작전판 6920 스포츠/레저>스포츠액세서리>스코어보드/작전판'</li><li>'축구작전판 풋살작전판 전술보드 지휘판 축구판 s224 2027A E13WCB087EM 스포츠/레저>스포츠액세서리>스코어보드/작전판'</li></ul>                                          |
+| 8.0   | <ul><li>'아이워너 범블비 전자호각 호루라기 KS-848 스포츠/레저>스포츠액세서리>호각/호루라기'</li><li>'프로맥스 스포츠호각-그린 스포츠/레저>스포츠액세서리>호각/호루라기'</li><li>'Star 호각 플라스틱 스포츠/레저>스포츠액세서리>호각/호루라기'</li></ul>                                                                                                      |
+| 6.0   | <ul><li>'런웨이브 스포츠 헤어밴드 1p 땀흡수 머리고정 운동 스포츠/레저>스포츠액세서리>스포츠헤어밴드'</li><li>'미즈노 스트레치 헤어밴드 33YZ210305 스포츠/레저>스포츠액세서리>스포츠헤어밴드'</li><li>'피엘라벤 아비스코 울 헤드밴드 F235UCA09AC 스포츠/레저>스포츠액세서리>스포츠헤어밴드'</li></ul>                                                                        |
+| 7.0   | <ul><li>'배달 등산 트래킹 UV차단 멀티스카프 여름목토시 아이스목도리 자전거마스크 쿨넥 스포츠/레저>스포츠액세서리>아이스머플러/스카프'</li><li>'자외선 통기성 귀걸이형 메쉬 스카��� 등산용마스크 등산스카프 스포츠/레저>스포츠액세서리>아이스머플러/스카프'</li><li>'해밀 4 1 국산 쿨스카프 넥쿨러 냉감 스포츠 아이스 머플러 자외선차단 여름 넥워머 등산 스포츠/레저>스포츠액세서리>아이스머플러/스카프'</li></ul>                   |
+| 5.0   | <ul><li>'내셔널지오그래픽 아이스 슬리브 N232AGL510 WHITE151842 스포츠/레저>스포츠액세서리>스포츠토시'</li><li>'여름 골프 쿨토시 UV 자외선차단 아이스 팔토시 남성 여성 암슬리브 등산 자전거 운전 손등토시 스포츠/레저>스포츠액세서리>스포츠토시'</li><li>'팔토시 여름 등산 토시 스포츠/레저>스포츠액세서리>스포츠토시'</li></ul>                                                       |
+| 3.0   | <ul><li>'야팩 자외선차단 골프마스크 얼굴햇빛가리개 등산 자전거 스포츠 아웃도어 마스크팩 1BOX 4매 스포츠/레저>스포츠액세서리>스포츠마스크'</li><li>'블랙야크 남여공용 여름 다용도 UV차단마스크 UV차양마스크 2BYXXX3912 597799 스포츠/레저>스포츠액세서리>스포츠마스크'</li><li>'유투스포츠 1 1 MS1 스탠다드 익스트림 마스크 자외선차단 골프 자전거 스포츠 등산 낚시 산책 스포츠/레저>스포츠액세서리>스포츠마스크'</li></ul> |
+| 4.0   | <ul><li>'바너 스포츠 고글 선글라스 자전거 미러 변색 안경 스포츠/레저>스포츠액세서리>스포츠선글라스'</li><li>'BRIKO CYCLOPE 썬글라스 904 스포츠/레저>스포츠액세서리>스포츠선글라스'</li><li>'WTD 스템 변색 미러 렌즈 선글라스 스포츠/레저>스포츠액세서리>스포츠선글라스'</li></ul>                                                                                   |
+| 0.0   | <ul><li>'체육관 공 정리함 수납함 철제 보관 바구니 학교 카트 스포츠/레저>스포츠액세서리>볼캐리어'</li><li>'농구 공 보관 랙 3 단 큐브 거치대 차고 스포츠 정리함 실내 탈착식 수직 디스플레이 스탠드 스포츠/레저>스포츠액세서리>볼캐리어'</li><li>'농구공보관함 체육관 학교 공 이동식 수납 정리대-모델굵은글씨화이트140x35x140 5레이어544 5457게이 스포츠/레저>스포츠액세서리>볼캐리어'</li></ul>                    |
+| 2.0   | <ul><li>'아크테릭스 23FW 로 라이트웨이트 울 바라클라바 BLK RHO 라이트 WEIGHT BALACLAVA M 95 정도이니 참조 AENFUX5968 스포츠/레저>스포츠액세서리>스포츠넥워머'</li><li>'블랙 바라클라바 자외선차단마스크 여름넥워머 스포츠/레저>스포츠액세서리>스포츠넥워머'</li><li>'NATIONALGEOGRAPHIC 넥게이터 N232AAC510 스포츠/레저>스포츠액세서리>스포츠넥워머'</li></ul>                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9989   |
+
+## 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_sl21")
+# Run inference
+preds = model("등산 자전거 바이크 멀티 스카프 스포츠/레저>스포츠액세서리>아이스머플러/스카프")
+```
+
+<!--
+### 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   | 3   | 8.4524 | 21  |
+
+| 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                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0081  | 1    | 0.5163        | -               |
+| 0.4032  | 50   | 0.4971        | -               |
+| 0.8065  | 100  | 0.3704        | -               |
+| 1.2097  | 150  | 0.1312        | -               |
+| 1.6129  | 200  | 0.0318        | -               |
+| 2.0161  | 250  | 0.0173        | -               |
+| 2.4194  | 300  | 0.0096        | -               |
+| 2.8226  | 350  | 0.0001        | -               |
+| 3.2258  | 400  | 0.0001        | -               |
+| 3.6290  | 450  | 0.0001        | -               |
+| 4.0323  | 500  | 0.0           | -               |
+| 4.4355  | 550  | 0.0           | -               |
+| 4.8387  | 600  | 0.0           | -               |
+| 5.2419  | 650  | 0.0           | -               |
+| 5.6452  | 700  | 0.0           | -               |
+| 6.0484  | 750  | 0.0           | -               |
+| 6.4516  | 800  | 0.0           | -               |
+| 6.8548  | 850  | 0.0           | -               |
+| 7.2581  | 900  | 0.0           | -               |
+| 7.6613  | 950  | 0.0           | -               |
+| 8.0645  | 1000 | 0.0           | -               |
+| 8.4677  | 1050 | 0.0           | -               |
+| 8.8710  | 1100 | 0.0           | -               |
+| 9.2742  | 1150 | 0.0           | -               |
+| 9.6774  | 1200 | 0.0           | -               |
+| 10.0806 | 1250 | 0.0           | -               |
+| 10.4839 | 1300 | 0.0           | -               |
+| 10.8871 | 1350 | 0.0           | -               |
+| 11.2903 | 1400 | 0.0           | -               |
+| 11.6935 | 1450 | 0.0           | -               |
+| 12.0968 | 1500 | 0.0           | -               |
+| 12.5    | 1550 | 0.0           | -               |
+| 12.9032 | 1600 | 0.0           | -               |
+| 13.3065 | 1650 | 0.0           | -               |
+| 13.7097 | 1700 | 0.0           | -               |
+| 14.1129 | 1750 | 0.0           | -               |
+| 14.5161 | 1800 | 0.0           | -               |
+| 14.9194 | 1850 | 0.0           | -               |
+| 15.3226 | 1900 | 0.0           | -               |
+| 15.7258 | 1950 | 0.0           | -               |
+| 16.1290 | 2000 | 0.0           | -               |
+| 16.5323 | 2050 | 0.0           | -               |
+| 16.9355 | 2100 | 0.0           | -               |
+| 17.3387 | 2150 | 0.0           | -               |
+| 17.7419 | 2200 | 0.0           | -               |
+| 18.1452 | 2250 | 0.0           | -               |
+| 18.5484 | 2300 | 0.0           | -               |
+| 18.9516 | 2350 | 0.0           | -               |
+| 19.3548 | 2400 | 0.0           | -               |
+| 19.7581 | 2450 | 0.0           | -               |
+| 20.1613 | 2500 | 0.0           | -               |
+| 20.5645 | 2550 | 0.0           | -               |
+| 20.9677 | 2600 | 0.0           | -               |
+| 21.3710 | 2650 | 0.0           | -               |
+| 21.7742 | 2700 | 0.0           | -               |
+| 22.1774 | 2750 | 0.0           | -               |
+| 22.5806 | 2800 | 0.0           | -               |
+| 22.9839 | 2850 | 0.0           | -               |
+| 23.3871 | 2900 | 0.0           | -               |
+| 23.7903 | 2950 | 0.0           | -               |
+| 24.1935 | 3000 | 0.0           | -               |
+| 24.5968 | 3050 | 0.0           | -               |
+| 25.0    | 3100 | 0.0           | -               |
+| 25.4032 | 3150 | 0.0           | -               |
+| 25.8065 | 3200 | 0.0           | -               |
+| 26.2097 | 3250 | 0.0           | -               |
+| 26.6129 | 3300 | 0.0           | -               |
+| 27.0161 | 3350 | 0.0           | -               |
+| 27.4194 | 3400 | 0.0           | -               |
+| 27.8226 | 3450 | 0.0           | -               |
+| 28.2258 | 3500 | 0.0           | -               |
+| 28.6290 | 3550 | 0.0           | -               |
+| 29.0323 | 3600 | 0.0           | -               |
+| 29.4355 | 3650 | 0.0           | -               |
+| 29.8387 | 3700 | 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_sl_org_gtcate",
+  "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 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5611dc52b1b183f807a019abdf189ee92f5379868e6910e40433335855f6c1ea
+size 442494816

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4dd4d3b3fd6a0e81a155d8cae4d600d32bfcd62add3f5513acac9fff1ec86d59
+size 56255

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}