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 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 45T PVC 원톤파티션 사무실파티션 책상 칸막이 패브릭 천파티션 가림막 W600 H1000 가구/인테리어>서재/사무용가구>사무/교구용가구>파티션
+- text: GOYA 고야 크맘 곰 자작나무 책상 파티션 600 학교 칸막이 가구/인테리어>서재/사무용가구>사무/교구용가구>파티션
+- text: 와이디 로아 모던 책상 미드센츄리 테이블  800 가구/인테리어>서재/사무용가구>책상>일자형 책상
+- text: 컴퓨터 의자 가정용 앉은 기숙사 대학생 소파 사무실 거짓말 가구/인테리어>서재/사무용가구>의자>하이팩의자
+- text: 한샘 레그핏 쿠션형 책상 발받침대 의자발받침 다리받침대 가구/인테리어>서재/사무용가구>의자>의자발받침대
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: mini1013/master_domain
+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: 1.0
+      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:** 5 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                                                                                                                                                                                                       |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 4.0   | <ul><li>'스코나 밀러튼 LPM 1400 멀티 교구장 책장 가구/인테리어>서재/사무용가구>책장'</li><li>'이케아 BILLY 빌리 3단 책장 40cm 가구/인테리어>서재/사무용가구>책장'</li><li>'에보니아 로엠 600 3단 하부 도어 책장 가구/인테리어>서재/사무용가구>책장'</li></ul>                                 |
+| 2.0   | <ul><li>'선반 철제 책꽂이 수납 타공판 책상위정리 책장 세트-후크 3 흰색 단층 홀 보드 가구/인테리어>서재/사무용가구>책꽂이'</li><li>'델리 2단 서랍 겸 책꽂이 데스크 손잡이 오거나이저 가구/인테리어>서재/사무용가구>책꽂이'</li><li>'북케이스 책장 수납 선반 북 보관 책꽂이 가구/인테리어>서재/사무용가구>책꽂이'</li></ul>        |
+| 3.0   | <ul><li>'209애비뉴 제로데스크 에보 멀티 컴퓨터책상 1600x800 가구/인테리어>서재/사무용가구>책상>컴퓨터책상'</li><li>'한샘 티오 일자책상세트 5단 120x60cm 콘센트형 조명 가구/인테리어>서재/사무용가구>책상>일자형 책상'</li><li>'아씨방 마일드 모션데스크 120cm 가구/인테리어>서재/사무용가구>책상>스탠딩책상'</li></ul>  |
+| 0.0   | <ul><li>'하이솔로몬 강의대 LS13 가구/인테리어>서재/사무용가구>사무/교구용가구>사무용책상'</li><li>'사무실쇼파 제논 2인용 소파 가구/인테리어>서재/사무용가구>사무/교구용가구>사무용소파'</li><li>'스테인리스 서랍장 캐비닛 미용실 매장용 사물함 스텐 가구/인테리어>서재/사무용가구>사무/교구용가구>캐비닛'</li></ul>              |
+| 1.0   | <ul><li>'접이식 썬베드 간이 낮잠 의자 휴대용 리클라이너 경량 가구/인테리어>서재/사무용가구>의자>안락의자'</li><li>'체스좌식의자 엠보싱 가구/인테리어>서재/사무용가구>의자>좌식의자'</li><li>'나른인 쇼파 손잡이가 달린 침대 위 나부끼창 커밋의자 껴안다 건산수유 의자와 다다미 좌석 가구/인테리어>서재/사무용가구>의자>하이팩의자'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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_fi3")
+# Run inference
+preds = model("와이디 로아 모던 책상 미드센츄리 테이블  800 가구/인테리어>서재/사무용가구>책상>일자형 책상")
+```
+
+<!--
+### 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   | 2   | 8.5543 | 22  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+
+### Training Hyperparameters
+- batch_size: (256, 256)
+- num_epochs: (30, 30)
+- 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0145  | 1    | 0.4825        | -               |
+| 0.7246  | 50   | 0.4985        | -               |
+| 1.4493  | 100  | 0.4783        | -               |
+| 2.1739  | 150  | 0.1925        | -               |
+| 2.8986  | 200  | 0.0024        | -               |
+| 3.6232  | 250  | 0.0001        | -               |
+| 4.3478  | 300  | 0.0001        | -               |
+| 5.0725  | 350  | 0.0001        | -               |
+| 5.7971  | 400  | 0.0           | -               |
+| 6.5217  | 450  | 0.0           | -               |
+| 7.2464  | 500  | 0.0           | -               |
+| 7.9710  | 550  | 0.0           | -               |
+| 8.6957  | 600  | 0.0           | -               |
+| 9.4203  | 650  | 0.0           | -               |
+| 10.1449 | 700  | 0.0           | -               |
+| 10.8696 | 750  | 0.0           | -               |
+| 11.5942 | 800  | 0.0           | -               |
+| 12.3188 | 850  | 0.0           | -               |
+| 13.0435 | 900  | 0.0           | -               |
+| 13.7681 | 950  | 0.0           | -               |
+| 14.4928 | 1000 | 0.0           | -               |
+| 15.2174 | 1050 | 0.0           | -               |
+| 15.9420 | 1100 | 0.0           | -               |
+| 16.6667 | 1150 | 0.0           | -               |
+| 17.3913 | 1200 | 0.0           | -               |
+| 18.1159 | 1250 | 0.0           | -               |
+| 18.8406 | 1300 | 0.0           | -               |
+| 19.5652 | 1350 | 0.0           | -               |
+| 20.2899 | 1400 | 0.0           | -               |
+| 21.0145 | 1450 | 0.0           | -               |
+| 21.7391 | 1500 | 0.0           | -               |
+| 22.4638 | 1550 | 0.0           | -               |
+| 23.1884 | 1600 | 0.0           | -               |
+| 23.9130 | 1650 | 0.0           | -               |
+| 24.6377 | 1700 | 0.0           | -               |
+| 25.3623 | 1750 | 0.0           | -               |
+| 26.0870 | 1800 | 0.0           | -               |
+| 26.8116 | 1850 | 0.0           | -               |
+| 27.5362 | 1900 | 0.0           | -               |
+| 28.2609 | 1950 | 0.0           | -               |
+| 28.9855 | 2000 | 0.0           | -               |
+| 29.7101 | 2050 | 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

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+{
+  "_name_or_path": "mini1013/master_item_fi",
+  "architectures": [
+    "RobertaModel"
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+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "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": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
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+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+  "labels": null
+}

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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+    "path": "1_Pooling",
+    "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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