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 +1,243 @@
+---
+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: TT-S 1200 상면 여과기 포함_미포함_포함 제스트에이(찌니으니네)
+- text: 레고 어항 마리모 베타 구피 물고기 키우기 수초 수조 용품 치어통 미니 수족관 피규어_7-04. 기타 / 요트 블루 주식회사 대성상사
+- text: 배수 수중 펌프 어항 바닥 물빼기 흡수 저수위 순환 20W워터펌프-수도관포함40-60cm수조에적합 조희파파
+- text: 리글라스 히터 X-511 ［50W］어항히터 수조히터 구피히터-수량한정세일  본아쿠아
+- text: 골든엘바진 7g x 1포 어항수질개선제  비오비
+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: 0.9091566265060241
+      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                                                                                                                                                                                                         |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 10.0  | <ul><li>'클리오네 커버형 히터300W 완전 방수 방폭 히터 (AT-600)  마이팀'</li><li>'(신형) 선선 히터 PID 디스플레이 가변 절전 AR-450(500w) 이츠트레이딩'</li><li>'노블 고급 스테인레스 히터 X-368 25W  신바람잡화점'</li></ul>                                                |
+| 2.0   | <ul><li>'6구원통형 황토산란상 새우 치어 은신처 장식 HD-1019  '</li><li>'리선 자동 먹이 급여기 AF-2003  다선배'</li><li>'지스 대용량 브라인쉬림프 부화기[알에이디 주식회사]  주식회사 웹이즈'</li></ul>                                                                      |
+| 0.0   | <ul><li>'청룡아쿠아 SICCE 수중모터8W SYNCRA 0.5 시세수중펌프 어항 수족관  경승무역'</li><li>'물 펌프 수영장 빼기 흡입 저소음 배수 바닥 자동 5W양수펌프-호스포함10-20cm어항적합 정직한셀러15'</li><li>'협신7W(UP-70)  스타릿컴퍼니(Starlit Co.)'</li></ul>                            |
+| 4.0   | <ul><li>'아쿠아타운 미니 블럭 어항 6구 보급형 레고 베타 새우 소형 수족관 어린이집 방과후 미술학원 마리모 반투명 어장관리의정석'</li><li>'어항 선반 튼튼한 수조 다이 받침대 거치대 축양장 길이 50x너비 40x70 높이 달고나마켓'</li><li>'칸후 올디아망 어항 35슬림B 35x26x30cm(5T) 물고기수조  알에이디 주식회사'</li></ul> |
+| 9.0   | <ul><li>'리글라스 UVA UVB 할로겐램프 25w 파충류 거북이램프  나나아쿠아'</li><li>'이스타 거북이 할로겐 조명소켓  뽐아이뽐'</li><li>'아마존 LED 미니등 (CH-L5)[스타릿1] 화이트 주식회사 웹이즈'</li></ul>                                                                    |
+| 6.0   | <ul><li>'아마존 역류 방지기 /역류방지  해율'</li><li>'저압 원판 분사기 소 [지름6cm]  주식회사 그루터기'</li><li>'정화조 에어펌프 브로워 40리터 동양사 DY-40LL 경제형 오수처리시설 블로워 브로와  금홍환경'</li></ul>                                                               |
+| 3.0   | <ul><li>'아마존 원형 쉬림프망 뜰채 (중)  마이팀'</li><li>'켈란 산호&&치어 피딩용 스포이드 30cm / 총 39cm[K-072]  아마존수족관365'</li><li>'이스타 자동볼탑 보충수통 (AS-100S) 2L  마이팀'</li></ul>                                                               |
+| 5.0   | <ul><li>'심플 STEP 수초 레이아웃 전용 접착제 - 3g  제이디(JD) AQUA'</li><li>'열대어 여과재 박테리아제 [SL-Aqua 빙차우 V2] St 500ml (새우수조용 수초액비)  하나수족관'</li><li>'피알피쉬 1.5자 수초세트(45cm수조 수초세트)  피알코퍼레이션'</li></ul>                               |
+| 7.0   | <ul><li>'BASA 스펀지여과기 일체형 그레이  피알코퍼레이션'</li><li>'BASA 스펀지여과기 4종 미니 쌍기 부천수족관'</li><li>'에하임 프리필터 [400432]  플랜A아쿠아'</li></ul>                                                                                        |
+| 1.0   | <ul><li>'히카리 팬시구피 70g  물멍'</li><li>'테트라 폰드 코이 스틱 4L / 잉어 코이 사료  라임 펫 아쿠아'</li><li>'테트라 이니셜스틱 250ml / 수초뿌리비료  라임 펫 아쿠아'</li></ul>                                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9092 |
+
+## 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_lh3")
+# Run inference
+preds = model("골든엘바진 7g x 1포 어항수질개선제  비오비")
+```
+
+<!--
+### 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   | 9.21   | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 50                    |
+| 6.0   | 50                    |
+| 7.0   | 50                    |
+| 9.0   | 50                    |
+| 10.0  | 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    | 0.4167        | -               |
+| 0.6329  | 50   | 0.2604        | -               |
+| 1.2658  | 100  | 0.0829        | -               |
+| 1.8987  | 150  | 0.0561        | -               |
+| 2.5316  | 200  | 0.0433        | -               |
+| 3.1646  | 250  | 0.0513        | -               |
+| 3.7975  | 300  | 0.0157        | -               |
+| 4.4304  | 350  | 0.0273        | -               |
+| 5.0633  | 400  | 0.0175        | -               |
+| 5.6962  | 450  | 0.0175        | -               |
+| 6.3291  | 500  | 0.0175        | -               |
+| 6.9620  | 550  | 0.0195        | -               |
+| 7.5949  | 600  | 0.0252        | -               |
+| 8.2278  | 650  | 0.0252        | -               |
+| 8.8608  | 700  | 0.0193        | -               |
+| 9.4937  | 750  | 0.0283        | -               |
+| 10.1266 | 800  | 0.0136        | -               |
+| 10.7595 | 850  | 0.005         | -               |
+| 11.3924 | 900  | 0.0001        | -               |
+| 12.0253 | 950  | 0.0001        | -               |
+| 12.6582 | 1000 | 0.0001        | -               |
+| 13.2911 | 1050 | 0.0001        | -               |
+| 13.9241 | 1100 | 0.0001        | -               |
+| 14.5570 | 1150 | 0.0           | -               |
+| 15.1899 | 1200 | 0.0           | -               |
+| 15.8228 | 1250 | 0.0001        | -               |
+| 16.4557 | 1300 | 0.0           | -               |
+| 17.0886 | 1350 | 0.0001        | -               |
+| 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/master_item_lh",
+  "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 sha256:002bfa938d9e764020241242855e835fb873bd8c889780b72f1dc14f60846e2a
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00947ff3718ba5e67aeb3b529ea90ce7ce8ab6a7f0a9ae82bdb1737b6787a40d
+size 62407

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]"
+}