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 +1,249 @@
+---
+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: WD NEW MY PASSPORT 외장SSD 1TB 외장하드 스마트폰 아이패드 XBOX  세븐컴
+- text: '2.5인치 HDD SSD 보관 케이스 USB3.0 SATA 어답터 확장 외장하드 케이스 선택1: 2.5인치 HDD SSD 하드 보관함
+    퀄리티어슈어런스코리아'
+- text: 이지넷 NEXT-350U3 3.5 외장케이스/USB3.0 하드미포함  레알몰
+- text: NEXT-644DU3 4베이 HDD SSD USB3.0 도킹스테이션  프리줌
+- text: Seagate IronWolf NAS ST1000VN002 1TB AS3년/공식판매점  (주)픽셀아트 (PIXELART)
+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: 0.7785757031717534
+      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:** 12 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                                                                                                                                                                                                                                                      |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 3     | <ul><li>'키오시아 EXCERIA PLUS G3 M.2 NVMe  엄지척스토어'</li><li>'[키오시아] EXCERIA G2 M.2 NVMe (500GB)  주식회사 에티버스이비티'</li><li>'ADATA Ultimate SU650 120GB  밀알시스템'</li></ul>                                                                                              |
+| 1     | <ul><li>'시놀로지 Expansion Unit DX517 (5베이/하드미포함) 타워형 확장 유닛 DS1817+, DS1517+  (주)비엔지센터'</li><li>'[아이피타임 쇼핑몰] NAS1 dual 1베이 나스 (하드미포함)  (주)에이치앤인터내셔널'</li><li>'시놀로지 정품 나스 DS223 2베이 NAS 스토리지 클라우드 서버 구축 시놀로지 NAS DS223 유심홀릭'</li></ul>                            |
+| 0     | <ul><li>'씨게이트 바라쿠다 1TB ST1000DM010 SATA3 64M 1테라 하드 오늘 출발  주식회사 호스트시스템'</li><li>'WD BLUE (WD20EZBX) 3.5 SATA HDD (2TB/7200rpm/256MB/SMR)  아이코다(주)'</li><li>'씨게이트 IronWolf 8TB ST8000VN004 (SATA3/7200/256M)  (주)조이젠'</li></ul>                                |
+| 4     | <ul><li>'Sandisk Extreme Pro CZ880 (128GB)  (주)아이티엔조이'</li><li>'Sandisk Cruzer Glide CZ600 (16GB)  컴튜브 주식회사'</li><li>'샌디스크 울트라 핏 USB 3.1 32GB Ultra Fit CZ430 초소형  주식회사 에스티원테크'</li></ul>                                                                     |
+| 6     | <ul><li>'NEXT-DC3011TS 1:11 HDD SSD 스마트 하드복사 삭제기  리벤플러스'</li><li>'넥시 NX-802RU31 2베이 RAID 데이터 스토리지 하드 도킹스테이션 (NX768)  대성NETWORK'</li><li>'넥시 USB3.1 C타입 2베이 DAS 데이터 스토리지 NX768  (주)팁스커뮤니케이션즈'</li></ul>                                                        |
+| 11    | <ul><li>'이지넷유비쿼터스 NEXT-215U3 (하드미포함)  (주)컴파크씨앤씨'</li><li>'ORICO PHP-35 보라 3.5인치 하드 보호케이스  (주)조이젠'</li><li>'[ORICO] PHP-35 3.5형 하드디스크 보관함 [블루]  (주)컴퓨존'</li></ul>                                                                                              |
+| 2     | <ul><li>'(주)근호컴 [라인업시스템]LS-EXODDC 외장ODD  (주)근호컴'</li><li>'[라인업시스템] LANSTAR LS-BRODD 블루레이 외장ODD  주식회사 에티버스이비티'</li><li>'넥스트유 NEXT-200DVD-RW USB3.0 DVD-RW 드라이브 )  (주)인컴씨엔에스'</li></ul>                                                                         |
+| 5     | <ul><li>'(주)근호컴 [멜로디]1P 투명 연질 CD/DVD 케이스 (10장)  (주)근호컴'</li><li>'HP CD-R 10P / 52X 700MB / 원통케이스 포장 제품  티앤제이 (T&J) 통상'</li><li>'엑토 CD롬컨테이너_50매입 CDC-50K /CD보관함/CD케이스/씨디보관함/씨디케이스/cd정리함 CDC-50K 아이보리 솔로몬샵'</li></ul>                                           |
+| 9     | <ul><li>'시놀로지 비드라이브 BDS70-1T BeeDrive 1TB 외장SSD 개인 백업허브 정품  솔루션 웍스(Solution Works)'</li><li>'CORSAIR EX100U Portable SSD Type C (1TB)  (주)아이티엔조이'</li><li>'ASUS ROG STRIX ARION ESD-S1C M 2 NVMe SSD 외장케이스  (주)아이웍스'</li></ul>                                |
+| 8     | <ul><li>'넥스트유 NEXT-651DCU3 도킹스테이션 2베이  (주)수빈인포텍'</li><li>'이지넷유비쿼터스 넥스트유 659CCU3 도킹 스테이션  주식회사 매커드'</li><li>'이지넷유비쿼터스 NEXT-644DU3 4베이 도킹스테이션  에이치엠에스'</li></ul>                                                                                                |
+| 10    | <ul><li>'USB3.0 4베이 DAS 스토리지 NX770  (주)담다몰'</li><li>'[NEXI] NX-804RU30 외장 케이스 HDD SSD USB 3.0 4베이 하드 도킹스테이션 NX770  주식회사 유진정보통신'</li><li>'[NEXI] 넥시 NX-804RU30 RAID (4베이) [USB3.0] [NX770] [DAS] [하드미포함]  (주)컴퓨존'</li></ul>                                    |
+| 7     | <ul><li>'USB3.0 하드 도킹스테이션 복제 복사 클론 복사기 HDD SSD 2.5인치 3.5인치 듀얼 외장하드 케이스 Q6GCLONE  퀄리티어슈런스'</li><li>'USB3.0 하드 도킹스테이션 복제 복사 클론 복사기 HDD SSD 2.5인치 3.5인치 듀얼 외장하드 케이스 28TB지원  퀄리티어슈런스'</li><li>'NEXT 652DCU3 HDD복제기능탑재/도킹스테이션/2.5인치/3.5인치/백업/클론기능  마하링크'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.7786 |
+
+## 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_el16")
+# Run inference
+preds = model("이지넷 NEXT-350U3 3.5 외장케이스/USB3.0 하드미포함  레알몰")
+```
+
+<!--
+### 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   | 9.6059 | 20  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 50                    |
+| 2     | 50                    |
+| 3     | 50                    |
+| 4     | 50                    |
+| 5     | 50                    |
+| 6     | 50                    |
+| 7     | 3                     |
+| 8     | 50                    |
+| 9     | 50                    |
+| 10    | 7                     |
+| 11    | 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0125 | 1    | 0.497         | -               |
+| 0.625  | 50   | 0.2348        | -               |
+| 1.25   | 100  | 0.0733        | -               |
+| 1.875  | 150  | 0.0254        | -               |
+| 2.5    | 200  | 0.0165        | -               |
+| 3.125  | 250  | 0.0122        | -               |
+| 3.75   | 300  | 0.0021        | -               |
+| 4.375  | 350  | 0.0024        | -               |
+| 5.0    | 400  | 0.001         | -               |
+| 5.625  | 450  | 0.0019        | -               |
+| 6.25   | 500  | 0.0002        | -               |
+| 6.875  | 550  | 0.0007        | -               |
+| 7.5    | 600  | 0.0009        | -               |
+| 8.125  | 650  | 0.0002        | -               |
+| 8.75   | 700  | 0.0002        | -               |
+| 9.375  | 750  | 0.0003        | -               |
+| 10.0   | 800  | 0.0002        | -               |
+| 10.625 | 850  | 0.0002        | -               |
+| 11.25  | 900  | 0.0002        | -               |
+| 11.875 | 950  | 0.0001        | -               |
+| 12.5   | 1000 | 0.0001        | -               |
+| 13.125 | 1050 | 0.0001        | -               |
+| 13.75  | 1100 | 0.0001        | -               |
+| 14.375 | 1150 | 0.0001        | -               |
+| 15.0   | 1200 | 0.0001        | -               |
+| 15.625 | 1250 | 0.0001        | -               |
+| 16.25  | 1300 | 0.0001        | -               |
+| 16.875 | 1350 | 0.0001        | -               |
+| 17.5   | 1400 | 0.0001        | -               |
+| 18.125 | 1450 | 0.0001        | -               |
+| 18.75  | 1500 | 0.0001        | -               |
+| 19.375 | 1550 | 0.0001        | -               |
+| 20.0   | 1600 | 0.0001        | -               |
+
+### 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/master_item_el",
+  "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": "4.46.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__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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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:3e43054e0c0a06ac231514e62996b39147567b7045a71cb454519332cf7d1c09
+size 442494816

model_head.pkl

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+size 74759

modules.json

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+[
+  {
+    "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

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

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "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

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
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+      "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]"
+}