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 +1,235 @@
+---
+base_model: mini1013/master_domain
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 듀크레이 덱시안 메드 아이리드 크림 15ml 피부과 옵션없음 비타콕
+- text: 라벤더 일회용 여성로션 3ML 옵션없음 동양유통
+- text: KAHI 멀티밤 리필키트 x 2개 옵션없음 에프엔지트렌드
+- text: 토니어 유기농 호호바 오일 30ml 옵션없음 주식회사 아람케이
+- text: 치카이치코 누드 판타지 화이트닝 크림 55ml 옵션없음 다물다선
+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: accuracy
+      value: 0.821590909090909
+      name: Accuracy
+---
+
+# 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:** 11 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                                                                                                                                                                                                   |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'가히 멀티 밤 리필형 9g x 1개(본품) + 9g x 3개(리필) 옵션없음 주식회사 제이제이몰'</li><li>'김정문알로에 큐어플러스 인텐시브 2x 크림 50g 3개 옵션없음 리틀리아'</li><li>'Good Molecules 젠틀 레티놀 크림 레티놀과 바쿠치올이 함유된 나이트 과색소 침 옵션없음 비포유'</li></ul>          |
+| 1.0   | <ul><li>'크리니크 드라마티컬리 디퍼런트 모이스처라이징 젤 125ml(건성, 중복합) 옵션없음 옐로우로켓'</li><li>'크리니크 드라마티컬리 디퍼런트 모이스처라이징 젤 125ml(건성, 중복합성) 옵션없음 샹양무역 유한회사'</li><li>'[케이스훼손] 더 후 공진향 인양 로션 110ml (케이스훼손) 인양 로션. 주식회사 포러스'</li></ul> |
+| 10.0  | <ul><li>'한율 송담 탄력 기초 2종 세트 (스킨+에멀젼) 기초 스킨 로션 여성 부모님화장품 스킨+에멀젼+아이크림+크림 홈뷰티샵'</li><li>'오휘 더 퍼스트 제너츄어 3종 스페셜 세트 옵션없음 브라우니박스2'</li><li>'쟝블랑 그린티 밸런싱 여성 3종세트 옵션없음 아 이리스'</li></ul>                              |
+| 7.0   | <ul><li>'[SKINFOOD] 캐롯 카로틴 카밍 워터패드 30매 (NEW 집게+패드케이스 ) 당근 (주)더블유컨셉코리아'</li><li>'메디힐 티트리 트러블 패드 100매 + 리필 100매 옵션없음 미뇨네'</li><li>'프리업 원더 포어 클리어 패드 휴대용 키트 10개입 옵션없음 주식회사 브랜드커머스'</li></ul>                  |
+| 4.0   | <ul><li>'에뛰드 모이스트풀 콜라겐 아이 크림 28ml Moistfull Collagen Eye Cream 옵션없음 월드세븐'</li><li>'마티나겝하르트 아보카도 아이크림 15ml 옵션없음 포비티엘'</li><li>'가히 아이밤 옵션없음 남영오'</li></ul>                                                   |
+| 9.0   | <ul><li>'안나홀츠 호호바오일 에코서트인증 유기농 압착 비정제 천연 호호바오일 60ml 2병 옵션���음 (주)안나홀츠'</li><li>'스킨아이 유기농 티트리 오일 옵션없음 폴슨 주식회사(FOLSN Inc.)'</li><li>'[3개세트] 유기농 티트리 오일 10ml 옵션없음 주식회사 보나쥬르'</li></ul>                          |
+| 0.0   | <ul><li>'멀티밤스틱 주름지우개 보툴레닌 기가스틱 넥스젠바이오'</li><li>'벨라수 데콜테 넥크림 50ml 벨라수'</li><li>'종근당 CKD 레티노 콜라겐 저분자 300 괄사 목주름 크림 50ml 동의함 일랑팩토리'</li></ul>                                                                 |
+| 8.0   | <ul><li>'AHC 누드톤업크림 내추럴글로우 40ml 옵션없음 가온'</li><li>'AHC 아우라 시크릿 톤업크림 50g 옵션없음 마리공주'</li><li>'AHC 톤업크림 아우라 시크릿 50g 옵션없음 쇼핑사거리'</li></ul>                                                                      |
+| 2.0   | <ul><li>'자트인사이트 울트라 셋팅 진짜 픽서 50ml 2개 옵션없음 솔마켓'</li><li>'ECLADO (1+1) NK-CX 프로틴 포텐 부스터 100ml 뿌리는 단백질 [1+1]NK-CX 포텐부스터 하이그래'</li><li>'CNP 차앤박 프로폴리스 에너지 앰플 미스트 250ml 1개 옵션없음 주식회사 아이지비'</li></ul>            |
+| 3.0   | <ul><li>'네이처리퍼블릭 리얼 스퀴즈 알로에 베라 토너 150ml(신형) 옵션없음 마켓유'</li><li>'허브 솔루션 위치하젤 토너 500ml / 1개 허브 솔루션 알로에 베라 토너 500ml 듀얼샵'</li><li>'르네셀 멀티 펩타이드 토너(재고정리) 옵션없음 숙이네 잡화'</li></ul>                                  |
+| 5.0   | <ul><li>'브링그린 알로에 99% 수딩 젤 300ml(민감성)/JL 옵션없음 주식회사 제이엘'</li><li>'브링그린 알로에 99% 수딩젤 300ml 옵션없음 모현'</li><li>'350211 포어 슈링커 바쿠치올 세럼 50ml 옵션없음 제이에프무역'</li></ul>                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8216   |
+
+## 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_bt8_test")
+# Run inference
+preds = model("라벤더 일회용 여성로션 3ML 옵션없음 동양유통")
+```
+
+<!--
+### 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   | 9.2179 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 18                    |
+| 1.0   | 18                    |
+| 2.0   | 22                    |
+| 3.0   | 20                    |
+| 4.0   | 32                    |
+| 5.0   | 30                    |
+| 6.0   | 40                    |
+| 7.0   | 23                    |
+| 8.0   | 17                    |
+| 9.0   | 14                    |
+| 10.0  | 23                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (40, 40)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 50
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0385  | 1    | 0.4822        | -               |
+| 1.9231  | 50   | 0.3286        | -               |
+| 3.8462  | 100  | 0.0503        | -               |
+| 5.7692  | 150  | 0.028         | -               |
+| 7.6923  | 200  | 0.0213        | -               |
+| 9.6154  | 250  | 0.0084        | -               |
+| 11.5385 | 300  | 0.0002        | -               |
+| 13.4615 | 350  | 0.0001        | -               |
+| 15.3846 | 400  | 0.0001        | -               |
+| 17.3077 | 450  | 0.0001        | -               |
+| 19.2308 | 500  | 0.0001        | -               |
+| 21.1538 | 550  | 0.0001        | -               |
+| 23.0769 | 600  | 0.0001        | -               |
+| 25.0    | 650  | 0.0001        | -               |
+| 26.9231 | 700  | 0.0           | -               |
+| 28.8462 | 750  | 0.0           | -               |
+| 30.7692 | 800  | 0.0           | -               |
+| 32.6923 | 850  | 0.0           | -               |
+| 34.6154 | 900  | 0.0           | -               |
+| 36.5385 | 950  | 0.0           | -               |
+| 38.4615 | 1000 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.44.2
+- PyTorch: 2.2.0a0+81ea7a4
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## 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_bt_test",
+  "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": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.2.0a0+81ea7a4"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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 sha256:8110a7c420ae7917f1b0bb50097a0cdb5eded2d580344aebf863e4f158062d1f
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d575e24a235e5ea2dbff875ab2b198312f2076eedb67f22a963c0cbb8010191e
+size 68575

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]"
+}