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

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+---
+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: HDTOP USB3.0 to HDMI 4K 영상 캡처보드15cm/HT-3C009/입력 4K 60Hz/녹화 1080P 60Hz/딜레이
+    없는 실시간 녹화/알루미늄 하우징/금도금 커넥터  디피시스템
+- text: 넥시 CAP02 USB HDMI 캡쳐보드 젠더타입  주식회사 디앤에스티
+- text: 블랙매직 DeckLink 8K Pro 덱링크 8k pro  디지탈A/V세상
+- text: 브리츠 BZ-SP600X 화이트 커브드 게이밍 사운드바  (주)에이치앤인터내셔널
+- text: AVerMedia Live Gamer 4K 2.1 GC575  초이스컴퓨터 주식회사
+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: 0.8028770510227017
+      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                                                                                                                                                                                                               |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 3     | <ul><li>'Britz 브리츠인터내셔널 BA-UMK120 다크실버 주식회사 꿈누리'</li><li>'Britz Accessories BA-R9 SoundBar 스피커 [화이트]  (주)조이젠'</li><li>'크리에이티브 PEBBLE V2  (주)아이티블루'</li></ul>                                                           |
+| 2     | <ul><li>'GN-2000S 구즈넥 마이크 콘덴서 (회의, 강연, 설교, 스피치, 교회, 법원, 방송)  사운드스토리'</li><li>'컴스 MT195 회의실용 콘덴서 마이크  아이코다(주)'</li><li>'고독스 EM68 RGB 카디오이드 USB 콘덴서 마이크 스탠드 / 납품 세금계산서 가능  주식회사 모즈인터내셔날'</li></ul>                       |
+| 8     | <ul><li>'레이저코리아 Razer Kiyo X 키요 X 웹캠 YT  주식회사 옐로우트리'</li><li>'앱코 APC930 QHD 웹캠 (블랙)  주식회사 동행하기'</li><li>'[병행,벌크]로지텍 C922 Pro Stream 웹캠  더블유에이취제이(WHJ)'</li></ul>                                                       |
+| 5     | <ul><li>'포커스라이트 스칼렛2i2 3세대 FocusriScarlett 2i2 3rd Gen  와이지스토어(주) (YG store Co., Ltd)'</li><li>'Focusrite 포커스라이트 Scarlett 18i8 3세대 오디오 인터페이스  씨엠뮤직(CM music)'</li><li>'크리에이티브 Creative 사운드 블라스터 X5  (주)아토닉스'</li></ul> |
+| 4     | <ul><li>'CORSAIR VOID RGB ELITE WIRELESS (화이트, 정품)  주식회사 꿈누리'</li><li>'TFG CH240 컬러풀 7.1Ch 게이밍헤드셋 (초경량 / 노이즈캔슬링 / 로스트아크) 블랙 (주)한성'</li><li>'로지텍 PRO X 2 LIGHTSPEED (핑크)  주식회사 조이쿨'</li></ul>                           |
+| 7     | <ul><li>'HD60X  주식회사 글렌트리'</li><li>'블랙매직 Blackmagic Design ATEM Mini Pro 아템미니프로  어썸팩토리(awesome factory)'</li><li>'AVerMedia ER330 EzRecorder PVR(독립형 녹화장치)  (주)스트림텍'</li></ul>                                         |
+| 0     | <ul><li>'이지넷유비쿼터스 NEXT-4516HDP 16채널 비디오 발룬 수신기  에이치엠에스'</li><li>'하이크비젼 DS-7604NI-K1/4P 4채널 IP POE NVR  CCTV테크'</li><li>'[HIKVISION 공식 수입원] 하이크비전 DS-7608NI-I2/8P UHD 4K IP카메라 네트워크 녹화기  (주)씨넥스존'</li></ul>             |
+| 6     | <ul><li>'스카이디지탈 DT-800 HDTV 안테나  (주)컴퓨존'</li><li>'(스카이디지탈) DT-800 HDTV 안테나 /안테나  엠지솔루션'</li><li>'무료 스카이디지탈 SKY DT-800 HDTV 지상파 안테나  주식회사에프엘인텍'</li></ul>                                                               |
+| 1     | <ul><li>'서진네트웍스 유니콘 AV-M9 UHD4K 안드로이드 셋탑박스 디빅스미디어플레이어 광고용디스플레이  (주)컴퓨존'</li><li>'유니콘 AV-M7 2세대 디빅스플레이어 UHD 4K지원 미디어플레이어  더원'</li><li>'서진네트웍스 UNICORN AV-M9 정품 멀티미디어 플레이어/영샵  영 샵'</li></ul>                            |
+| 9     | <ul><li>'옴니트로닉 MSP-Q1 2채널 휴대용 마이크스피커 핸드+핸드마이크 에이스전자'</li><li>'[공식] 에버미디어 AS311 Speakerphon 휴대용 스피커폰 AI 소음감지 USB전원  주식회사 이선디지탈'</li><li>'브리츠 BE-MC100 야외설치 아웃도어 방수 스피커  (주)담다몰'</li></ul>                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8029 |
+
+## 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_el8")
+# Run inference
+preds = model("넥시 CAP02 USB HDMI 캡쳐보드 젠더타입  주식회사 디앤에스티")
+```
+
+<!--
+### 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   | 9.3503 | 26  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 49                    |
+| 1     | 25                    |
+| 2     | 50                    |
+| 3     | 50                    |
+| 4     | 50                    |
+| 5     | 50                    |
+| 6     | 15                    |
+| 7     | 50                    |
+| 8     | 50                    |
+| 9     | 5                     |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0161  | 1    | 0.496         | -               |
+| 0.8065  | 50   | 0.2401        | -               |
+| 1.6129  | 100  | 0.0385        | -               |
+| 2.4194  | 150  | 0.025         | -               |
+| 3.2258  | 200  | 0.0181        | -               |
+| 4.0323  | 250  | 0.0004        | -               |
+| 4.8387  | 300  | 0.0002        | -               |
+| 5.6452  | 350  | 0.0001        | -               |
+| 6.4516  | 400  | 0.0002        | -               |
+| 7.2581  | 450  | 0.0001        | -               |
+| 8.0645  | 500  | 0.0001        | -               |
+| 8.8710  | 550  | 0.0001        | -               |
+| 9.6774  | 600  | 0.0001        | -               |
+| 10.4839 | 650  | 0.0001        | -               |
+| 11.2903 | 700  | 0.0001        | -               |
+| 12.0968 | 750  | 0.0           | -               |
+| 12.9032 | 800  | 0.0           | -               |
+| 13.7097 | 850  | 0.0           | -               |
+| 14.5161 | 900  | 0.0           | -               |
+| 15.3226 | 950  | 0.0           | -               |
+| 16.1290 | 1000 | 0.0           | -               |
+| 16.9355 | 1050 | 0.0           | -               |
+| 17.7419 | 1100 | 0.0           | -               |
+| 18.5484 | 1150 | 0.0           | -               |
+| 19.3548 | 1200 | 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,
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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "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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+    "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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+}