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 +1,220 @@
+---
+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: 신일 SVC-D500SR 무선청소기 싸이클론 유선형 이동식 본체 디자인 그린 워너비템
+- text: '[더트데빌 퀵플립플러스] 16V 리튬 무선 핸디청소기 (113년 전통/차량용/가정용/사무실/책상용/원룸/오피스텔)  (주)비즈온플레이스'
+- text: 신일전자 핸디형 무선 청소기 SVC-C27KP 차량용 가정용 소형청소기 원룸  새봄전자
+- text: 더트데빌 플립아웃 20V 리튬 무선 핸디청소기  (주)비즈온플레이스
+- text: 홈마블 진공 무선 핸디 미니 소형 스틱 청소기 화이트  씨엠케이(CMK)
+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: 0.8571428571428571
+      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                                                                                                                                                                                                                    |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2     | <ul><li>'셰퍼 스왈로우 핸디 청소기 JSK-N3009  주식회사 알앤피코스메틱'</li><li>'마이아 듀얼프로 에어건 무선 청소기 ND-MYA001W  박진희'</li><li>'신일 싸이클론 무선 청소기 SVC-D500SR 헤파필터 회전브러쉬  제이씨유통'</li></ul>                                                              |
+| 1     | <ul><li>'LG전자 코드제로 R5 로봇청소기 R585WKA1 (카밍 베이지)  엠제이테크'</li><li>'LG 코드제로 R5 R585WKA 로봇청소기 흡입 + 물걸레 / KN  (주)케이엔디지털'</li></ul>                                                                                                 |
+| 8     | <ul><li>'(전용) 한경희 무선청소기 HCV-B400 PRO 전용 클린타워 모터 보호 헤파필터  '</li><li>'삼성 정품 VS20B957F5E 청소기 헤드 흡입구 브러쉬 슬림 sava03291  H에이치마켓'</li><li>'[별제이]일렉트로룩스 청소기 호환용 먼지봉투 10매 S-BAG ZUS4065AF 호환용 먼지봉투 10매 신세계몰'</li></ul>               |
+| 5     | <ul><li>'디월트 20V 충전 스틱 전동 무선 청소기 차량용 세차용 집진기 DCV501LN  주식회사 신한비앤아이'</li><li>'디월트 집진기 충전 청소기 무선 세차기 차량용 스틱 업소용청소기 DCV501LN 본체만  라이프 공구'</li><li>'디월트 20V MAX 충전 스틱 집진 청소기 DCV501LN 01.DCV501LN 충전청소기 베어툴 한경툴 주식회사'</li></ul> |
+| 9     | <ul><li>'[신제품] 70도 열풍 진드기 제거 침구청소기 레이캅 코리아  '</li><li>'[신제품] 70도 열풍 진드기 제거 침구청소기 레이캅 코리아  '</li><li>'비쎌 스팟클린 하이드로 스팀 3791S 습식 스팀청소기 빈대퇴치 고온 살균 소파얼룩제거 BISSEL  '</li></ul>                                                   |
+| 7     | <ul><li>'[런칭기념 보관가방 ] 클링봇S 물분사 가성비 창문청소로봇 유리창 창문 베란다 로봇청소기  '</li><li>'에코백스 윈봇 W1S 창문 로봇청소기  에코백스공식스토어'</li><li>'클링봇S 물분사 창문로봇청소기 원조 창문 청소기 보관가방포함  아이뮤즈본사'</li></ul>                                                       |
+| 0     | <ul><li>'AVA 프리미엄 고압세척기 휴대용 가정용 고압세차기 AVA GO P55  동양테크툴'</li><li>'AVA 프리미엄 고압세척기 휴대용 가정용 고압세차기 아바 GO P55  에이지에스'</li></ul>                                                                                                  |
+| 6     | <ul><li>'신일 유선 싸이클론 진공청소기 SVC-R700LOT 레드 + 블랙_SVC-R700LOT 스위트코코'</li><li>'신일전자 유선 싸이클론 진공청소기 강력한흡입 HEPA필터 700W SVC-R700LOT  신창전자'</li><li>'이스타 먼지제로 유선 진공 청소기 핸디스틱 소형 원룸 가정용 ESK-WV400  주식회사 제이에스엘홀딩스'</li></ul>            |
+| 4     | <ul><li>'한경희 2in1 스팀청소기 HTE-S600 핸디스팀 LTE-S600 (주)에디샵'</li><li>'[2024년 최신형] 리빈치 초고속 예열 고온 살균 스팀청소기 LSC-200 리빈치 스팀청소기  + 추가패드 증정 총 4장 (주)바투네트워크'</li></ul>                                                                   |
+| 3     | <ul><li>'KAC-5000(유선형)/오토비스/자동물걸레청소기/국산정품/친환경제품/소비전력30W/1분당1000회이상/강력한함/ 1_화이트 클린랜드'</li><li>'WC-1500 무선 충전 물걸레 청소기 각도조절 세척 탈수통 제공 MinSellAmount 지큐아이씨앤씨'</li><li>'코맘스 소형 무선 물걸레청소기 그레이 PC9005G  보만코리아'</li></ul>           |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8571 |
+
+## 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_el19")
+# Run inference
+preds = model("더트데빌 플립아웃 20V 리튬 무선 핸디청소기  (주)비즈온플레이스")
+```
+
+<!--
+### 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   | 10.2791 | 18  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 2                     |
+| 1     | 2                     |
+| 2     | 50                    |
+| 3     | 5                     |
+| 4     | 2                     |
+| 5     | 6                     |
+| 6     | 14                    |
+| 7     | 9                     |
+| 8     | 26                    |
+| 9     | 13                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0476  | 1    | 0.4954        | -               |
+| 2.3810  | 50   | 0.0399        | -               |
+| 4.7619  | 100  | 0.0186        | -               |
+| 7.1429  | 150  | 0.0152        | -               |
+| 9.5238  | 200  | 0.0155        | -               |
+| 11.9048 | 250  | 0.0093        | -               |
+| 14.2857 | 300  | 0.0025        | -               |
+| 16.6667 | 350  | 0.0006        | -               |
+| 19.0476 | 400  | 0.0037        | -               |
+
+### 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

@@ -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:e60b60054f97029bd1dff23cd9e7f50eeb904e4ad13ad8e70619970921d71975
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8ebb04ba4cb95719a5695e212d16547d1c72bc52dd3726de42b778f9bef7627
+size 62439

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]"
+}