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 +1,236 @@
+---
+base_model: mini1013/master_domain
+library_name: setfit
+metrics:
+- metric
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Pulsar X2V2 미니 무선 게이밍 마우스 (블랙)  와이에스비투비
+- text: TOSHIBA B-EX4T2 바코드프린터 산업용프린터 라벨프린터 203DPI_USB ㈜비티에스홀딩스
+- text: '[당일출고]삼성전자 SL-J1680 컬러잉크젯 복합기 인쇄+복사+스캔 [정품잉크포함]  제일프린텍'
+- text: 지클릭커 슈퍼히어로 SPK100 저소음 유선 무선 블루투스 레인보우 백라이트 기계식 게임용 키보드 (레트로 레드)  (주)피씨베이스
+- text: NIIMBOT 님봇 D110 라벨기 휴대용 라벨프린터 라벨1롤포함  빅마운트앤컴퍼니
+inference: true
+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: metric
+      value: 0.8548111301103685
+      name: Metric
+---
+
+# 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                                                                                                                                                                                                                                     |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 7     | <ul><li>'와콤 CTL-472 웹툰 입문용 타블렛 펜 온라인강의  주식회사 지디스엠알오'</li><li>'와콤 타블렛 CTL-4100 와콤인튜어스 웹툰  (주)코티니'</li><li>'와콤 신티크16 DTK-1660  케이에이씨앤씨'</li></ul>                                                                                              |
+| 1     | <ul><li>'브라더공식판매대리점 DCP-T426W 무한잉크복합기 인쇄 복사 스캔 무선 AS연장  (주)대명아이티'</li><li>'교세라 ECOSYS M5521cdn 컬러레이저복합기 정품토너포함  한라테크'</li><li>'DCP-T720DW 브라더정품 무한잉크복합기 인쇄 복사 스캔 자동양면인쇄  (주)진전산시스템'</li></ul>                                                |
+| 4     | <ul><li>'로지텍 코리아 미니멀 무선 일루미네이티드 키보드 MX KEYS MINI 블랙(그라파이트)  주식회사 자강정보통신'</li><li>'앱코 K660 축교환 완전방수 게이밍 카일광축 레인보우LED 블랙,리니어  에스티에스컴퍼니'</li><li>'ABKO HACKER K523 기계식 축교환 LED 키패드  주식회사 브라보세컨즈'</li></ul>                                      |
+| 2     | <ul><li>'브라더 TN-2380 정품토너 2.6K HL L2365DW HL L2360dn MFC L2700D MFC L2700DW  주식회사 휴먼아이티'</li><li>'삼성전자정품 폐토너통 CLT-W406/ C510W/ C513W/ C563W/ C563FW  엘케이솔루션'</li><li>'(HP) No.680 정품 F6V27AA 검정 정품잉크 검정 총1개만구매(2개이상주문시발송안됨) 밀알시스템'</li></ul> |
+| 6     | <ul><li>'와콤원 펜 CP91300B2Z 삼성갤럭시탭,갤럭시노트,오닉스 호환 펜  '</li><li>'드로잉장갑 와콤 신티크 XP-PEN 휴이온 액정타블렛 아이패드 태블릿 터치오류방지  '</li><li>'��로잉장갑 와콤 신티크 XP-PEN 휴이온 액정타블렛 아이패드 태블릿 터치오류방지  '</li></ul>                                                             |
+| 8     | <ul><li>'◆◆ 정품 샘플테이프 + ◆◆ 브라더 正品 이름 라벨스티커기계 PT-P900W QR코드 wifi ◀正品▶ PT-P900W 탑정보기술'</li><li>'가제트 3D펜 GP3000+5M PLA 필라멘트 세트(24색)  (주)위드피플즈'</li><li>'인스탁스 와이드 링크 포토프린터 모카 그레이(+아크릴액자) 한국후지필름 (주)'</li></ul>                                   |
+| 3     | <ul><li>'엡손 DS-30000, 양면 스캐너 A3  주식회사 케이에스샵'</li><li>'엡손 WorkForce DS-50000  (주)테드이십일'</li><li>'엡손스캐너 ES-580WMLP 미니멀 라이프 패키지(ES-580W+재단기+롤러)북스캐너  (주)에이엔에이코리아'</li></ul>                                                                     |
+| 5     | <ul><li>'로지텍 MK295 SILENT WIRELESS COMBO (화이트)  (주)아토닉스'</li><li>'로지텍 MK275 영문자판 병행수입  제이제이 인터내셔널'</li><li>'로지텍코리아 시그니처 MK650 무선 합본 (그래파이트)  주식회사 지엠샤이'</li></ul>                                                                            |
+| 0     | <ul><li>'ROCCAT KONE PRO AIR (블랙)  (주)디아씨앤씨'</li><li>'[Logitech]로지텍 Trackman Marble USB 마우스 트랙맨 트랙볼 마블 마우스 벌크 /택배/병행/ 당일출고 Trackman Marble USB 허브포스트'</li><li>'로지텍 G402 Hyperion Fury  (주)케이엘시스템'</li></ul>                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8548 |
+
+## 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_el18")
+# Run inference
+preds = model("Pulsar X2V2 미니 무선 게이밍 마우스 (블랙)  와이에스비투비")
+```
+
+<!--
+### 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   | 4   | 10.5569 | 27  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 50                    |
+| 2     | 50                    |
+| 3     | 50                    |
+| 4     | 50                    |
+| 5     | 50                    |
+| 6     | 13                    |
+| 7     | 50                    |
+| 8     | 50                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (20, 20)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 40
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0154  | 1    | 0.4961        | -               |
+| 0.7692  | 50   | 0.1923        | -               |
+| 1.5385  | 100  | 0.0615        | -               |
+| 2.3077  | 150  | 0.0532        | -               |
+| 3.0769  | 200  | 0.0513        | -               |
+| 3.8462  | 250  | 0.0283        | -               |
+| 4.6154  | 300  | 0.0313        | -               |
+| 5.3846  | 350  | 0.0258        | -               |
+| 6.1538  | 400  | 0.0174        | -               |
+| 6.9231  | 450  | 0.0053        | -               |
+| 7.6923  | 500  | 0.0021        | -               |
+| 8.4615  | 550  | 0.0039        | -               |
+| 9.2308  | 600  | 0.0059        | -               |
+| 10.0    | 650  | 0.0001        | -               |
+| 10.7692 | 700  | 0.0001        | -               |
+| 11.5385 | 750  | 0.0001        | -               |
+| 12.3077 | 800  | 0.0001        | -               |
+| 13.0769 | 850  | 0.0001        | -               |
+| 13.8462 | 900  | 0.0           | -               |
+| 14.6154 | 950  | 0.0001        | -               |
+| 15.3846 | 1000 | 0.0           | -               |
+| 16.1538 | 1050 | 0.0           | -               |
+| 16.9231 | 1100 | 0.0           | -               |
+| 17.6923 | 1150 | 0.0           | -               |
+| 18.4615 | 1200 | 0.0           | -               |
+| 19.2308 | 1250 | 0.0           | -               |
+| 20.0    | 1300 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.46.1
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.20.0
+
+## 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

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+{
+  "_name_or_path": "mini1013/master_item_el",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "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": "4.46.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.46.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+    "single_word": false
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+}

tokenizer_config.json

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+}