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 +1,244 @@
+---
+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: 넥스트 USB 3.0 2포트 PCI Express 카드 (NEXT-212U3) YNMI-NK0431  윤 미디어
+- text: 앱코 NCORE G30 트루포스 (블랙) 미들타워 컴퓨터 케이스  오케이 바이오
+- text: APC SMC1500I-2U Smart UPS 900W/1500VA 무정전 전원공급장치 교체배터리 전원백업장치 (DHCNC)  주식회사
+    대현씨앤씨
+- text: 이지넷 카드리더기 NEXT-8603TCU3 블랙 [KF]  주식회사 케이에프컴퍼니
+- text: 다크플래쉬 darkFlash DS900 ARGB 강화유리 컴퓨터 PC 케이스 (블랙)  주식회사 아크런 (Akrun Co., Ltd.)
+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: 0.9098343017000216
+      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:** 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     | <ul><li>'APC BK500EI UPS배터리 무정전전원장치 300W 500VA  다피(dappy)'</li><li>'리안리 SP750 80PLUS GOLD (WHITE)  주식회사 브라보세컨즈'</li><li>'APC Smart UPS C 2000VA Tower 무정전전원장치 - smc2000ic  주식회사 파인인프라'</li></ul>                                                |
+| 2     | <ul><li>'3RSYS R200 RGB (블랙) 미들타워  컴온씨앤씨(주)'</li><li>'DAVEN AQUA (블랙)  주식회사 꿈누리'</li><li>'w 대원TMT DW-H1200 허브랙 (H1200×D800×W600/25U/회색) (착불배송)  (주)원영씨앤씨'</li></ul>                                                                             |
+| 0     | <ul><li>'인텔 코어i7-13세대 13700K 랩터레이크 정품 에어캡배송 (주)신우밀루유떼'</li><li>'AMD 라이젠5-4세대 5600X (버미어)벌크포장 AS 3년  태성에프앤비(주)'</li><li>'[INTEL] 코어10세대 i7-10700 벌크 병행 쿨러미포함 (코멧레이크)  (주)컴퓨존'</li></ul>                                                          |
+| 4     | <ul><li>'SAPPHIRE 라데온 RX 7900 GRE PURE D6 16GB  주식회사 꿈누리'</li><li>'ASRock 라데온 RX 7900 XTX Phantom Gaming OC D6 24GB 대원씨티에스  주식회사 에스씨엠인포텍'</li><li>'[HY] INNO3D 지포스 GT1030 D5 2GB LP 무소음  (주)제이케이존'</li></ul>                                    |
+| 8     | <ul><li>'잘만 ZM-STC10 (2g)  주식회사 피씨사자'</li><li>'3RSYS APB BAR 35  (주)컴퓨존'</li><li>'LP30 ARGB PSU 커버 화이트  주식회사보성닷컴'</li></ul>                                                                                                                     |
+| 6     | <ul><li>'NEXTU NEXT-206NEC EX  에스앤와이'</li><li>'LANstar PCI-E 내부 SATA3 4포트 카드/LS-PCIE-4SATA/PC 내부에 SATA3 4포트 생��/발열 방지용 방열판/LP 브라켓 포함  디피시스템'</li><li>'NEXTU NEXT-405NEC LP  에스앤와이'</li></ul>                                                     |
+| 3     | <ul><li>'V-Color BLACK DDR5-5200 CL42 STANDARD 벌크 (8GB)  (주)가이드컴'</li><li>'TEAMGROUP T-Force DDR5 6000 CL38 Delta RGB 화이트 패키지 32GB(16Gx2)  (주)서린씨앤아이'</li><li>'ADATA DDR5-5600 CL46 (16GB)/정품판매점/하이닉스A다이/언락/평생 제한 보증/R  주식회사 에이알씨앤아이'</li></ul> |
+| 5     | <ul><li>'ASRock H510M-HDV/M.2 SE 에즈윈  주식회사디케이'</li><li>'DK ASRock B760M PG Riptide D5 에즈윈  주식회사디케이'</li><li>'[ ] GIGABYTE B650 AORUS ELITE AX ICE 제이씨현  뉴비시스템즈'</li></ul>                                                                       |
+| 7     | <ul><li>'아틱 P14 PWM PST 블랙 VALUE 5팩  (주)서린씨앤아이'</li><li>'앱코 타이폰 120X5 CPU 쿨러 알루미늄 방열판  주식회사 지디스엠알오'</li><li>'Thermalright Peerless Assassin 120 SE 서린  태성에프앤비(주)'</li></ul>                                                                     |
+| 1     | <ul><li>'엠비에프 CAT.7 SFTP 금도금 UTP 3중 쉴드 패치코드 기가비트 랜케이블 0.5M (MBF-U705G)  주식회사 아크런 (Akrun Co., Ltd.)'</li><li>'MBF-C5E305R 305M 레드 BOX CAT.5E UTP 랜케이블  컴샷정보'</li><li>'엠비에프 CAT.5e UTP 제작형 랜케이블 박스 MBF-C5E305Y 옐로우 305m  (주)아토닉스'</li></ul>       |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9098 |
+
+## 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_el1")
+# Run inference
+preds = model("앱코 NCORE G30 트루포스 (블랙) 미들타워 컴퓨터 케이스  오케이 바이오")
+```
+
+<!--
+### 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   | 9.206  | 18  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 50                    |
+| 2     | 50                    |
+| 3     | 50                    |
+| 4     | 50                    |
+| 5     | 50                    |
+| 6     | 50                    |
+| 7     | 50                    |
+| 8     | 50                    |
+| 9     | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0127  | 1    | 0.4969        | -               |
+| 0.6329  | 50   | 0.2753        | -               |
+| 1.2658  | 100  | 0.0677        | -               |
+| 1.8987  | 150  | 0.014         | -               |
+| 2.5316  | 200  | 0.0023        | -               |
+| 3.1646  | 250  | 0.0001        | -               |
+| 3.7975  | 300  | 0.0001        | -               |
+| 4.4304  | 350  | 0.0001        | -               |
+| 5.0633  | 400  | 0.0001        | -               |
+| 5.6962  | 450  | 0.0           | -               |
+| 6.3291  | 500  | 0.0001        | -               |
+| 6.9620  | 550  | 0.0001        | -               |
+| 7.5949  | 600  | 0.0           | -               |
+| 8.2278  | 650  | 0.0           | -               |
+| 8.8608  | 700  | 0.0           | -               |
+| 9.4937  | 750  | 0.0           | -               |
+| 10.1266 | 800  | 0.0           | -               |
+| 10.7595 | 850  | 0.0           | -               |
+| 11.3924 | 900  | 0.0           | -               |
+| 12.0253 | 950  | 0.0           | -               |
+| 12.6582 | 1000 | 0.0           | -               |
+| 13.2911 | 1050 | 0.0           | -               |
+| 13.9241 | 1100 | 0.0           | -               |
+| 14.5570 | 1150 | 0.0           | -               |
+| 15.1899 | 1200 | 0.0           | -               |
+| 15.8228 | 1250 | 0.0           | -               |
+| 16.4557 | 1300 | 0.0           | -               |
+| 17.0886 | 1350 | 0.0           | -               |
+| 17.7215 | 1400 | 0.0           | -               |
+| 18.3544 | 1450 | 0.0           | -               |
+| 18.9873 | 1500 | 0.0           | -               |
+| 19.6203 | 1550 | 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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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "initializer_range": 0.02,
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
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+  "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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+    "type": "sentence_transformers.models.Transformer"
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+    "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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tokenizer_config.json

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+}