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 +1,236 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 쉬즈홈 썬키즈 시어서커 차렵이불 베개세트 S 가구/인테리어>침구세트>이불베개세트>싱글이불베개세트
+- text: 침구 이불베개세트 구스 밍크 홑이불토퍼세트 여름용 2컬러 가구/인테리어>침구세트>이불패드세트>슈퍼싱글이불패드세트
+- text: Q아라벨르 에린 극세사 침구세트 가구/인테리어>침구세트>이불베개세트>더블/퀸이불베개세트
+- text: 아망떼 아르카나 먼지없는 지퍼형 차렵이불커버 패드세트 가구/인테리어>침구세트>이불패드세트>슈퍼싱글이불패드세트
+- text: 세사 SESA 원스 알러지케어 이불베개세트 Q 가구/인테리어>침구세트>이불베개세트>싱글이불베개세트
+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:** 6 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                                                                                                                                                                                                                                       |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0.0   | <ul><li>'파코홈 굿메이드 300T 60수샤틴 홑겹이불커버 매트풀세트 SK 가구/인테리어>침구세트>매트커버세트>킹매트커버세트'</li><li>'마이하우스 알러지케어 디노랜드 키즈 사계절 이불 풀세트 S/SS 가구/인테리어>침구세트>매트커버세트>슈퍼싱글매트커버세트'</li><li>'마이하우스 알러지케어 래빗도트 핑크 키즈 차렵이불 풀세트 S/SS 가구/인테리어>침구세트>매트커버세트>슈퍼싱글매트커버세트'</li></ul> |
+| 5.0   | <ul><li>'한스데코 솔리드 극세사 차렵이불 세트 슈퍼싱글 1042880708 가구/인테리어>침구세트>한실예단세트'</li><li>'더이픽 바른 수면자세 서포트 무릎쿠션 메모리폼 쿠션 KA-A11125 가구/인테리어>침구세트>한실예단세트'</li><li>'특급 순면 꽃신상자 강면 꽃길이 솜털 수제솜 이불 속통 면주머니 목화솜 벌크업 면화 솜옷 가구/인테리어>침구세트>한실예단세트'</li></ul>              |
+| 2.0   | <ul><li>'설렘하우스 곰탱이 소프트 마이크로 사계절 이불베개세트 가구/인테리어>침구세트>이불베개세트>싱글이불베개세트'</li><li>'믹스앤매치 에센셜 스트라이프 듀라론 냉감 여름 차렵이불 베개커버 세트 Q 가구/인테리어>침구세트>이불베개세트>더블/퀸이불베개세트'</li><li>'드로잉에이미 drawing AMY 삐삐 차렵 이불 세트 가구/인테리어>침구세트>이불베개세트>싱글이불베개세트'</li></ul>         |
+| 3.0   | <ul><li>'파르페 파르페by알레르망 마틴 알러지케어 다운필 차렵이불���드세트 S 가구/인테리어>침구세트>이불패드세트>슈퍼싱글이불패드세트'</li><li>'운현궁 샤커 보니 패드세트Q 가구/인테리어>침구세트>이불패드세트>더블/퀸이불패드세트'</li><li>'레노마홈 뮤이모달 차렵이불 패드세트 Q 여름용 가구/인테리어>침구세트>이불패드세트>더블/퀸이불패드세트'</li></ul>                           |
+| 1.0   | <ul><li>'아망떼 15cm 더 커진 허밍가든 플라워 순면광목 차렵이불 요세트 Q 가구/인테리어>침구세트>요이불세트>2/3인용'</li><li>'아망떼 리틀가든 순면 차렵이불 요세트 SS 가구/인테리어>침구세트>요이불세트>1인용'</li><li>'아망떼 도티야 세미마이크로 차렵이불 요세트 Q 가구/인테리어>침구세트>요이불세트>2/3인용'</li></ul>                                      |
+| 4.0   | <ul><li>'라마 스커트 침대커버 세트 Q 가구/인테리어>침구세트>침대커버세트>더블/퀸침대커버세트'</li><li>'클라모프 텐셀 호텔 시트 커버 세트 Q 가구/인테리어>침구세트>침대커버세트>더블/퀸침대커버세트'</li><li>'러빙랩 다비치 레이스 침대커버 세트 Q 가구/인테리어>침구세트>침대커버세트>더블/퀸침대커버세트'</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_fi12")
+# Run inference
+preds = model("Q아라벨르 에린 극세사 침구세트 가구/인테리어>침구세트>이불베개세트>더블/퀸이불베개세트")
+```
+
+<!--
+### 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   | 8.4207 | 20  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 48                    |
+| 1.0   | 7                     |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 6                     |
+| 5.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0189  | 1    | 0.4779        | -               |
+| 0.9434  | 50   | 0.4942        | -               |
+| 1.8868  | 100  | 0.3341        | -               |
+| 2.8302  | 150  | 0.012         | -               |
+| 3.7736  | 200  | 0.0006        | -               |
+| 4.7170  | 250  | 0.0005        | -               |
+| 5.6604  | 300  | 0.0007        | -               |
+| 6.6038  | 350  | 0.0005        | -               |
+| 7.5472  | 400  | 0.0007        | -               |
+| 8.4906  | 450  | 0.0004        | -               |
+| 9.4340  | 500  | 0.0004        | -               |
+| 10.3774 | 550  | 0.0           | -               |
+| 11.3208 | 600  | 0.0           | -               |
+| 12.2642 | 650  | 0.0           | -               |
+| 13.2075 | 700  | 0.0           | -               |
+| 14.1509 | 750  | 0.0           | -               |
+| 15.0943 | 800  | 0.0           | -               |
+| 16.0377 | 850  | 0.0           | -               |
+| 16.9811 | 900  | 0.0           | -               |
+| 17.9245 | 950  | 0.0           | -               |
+| 18.8679 | 1000 | 0.0           | -               |
+| 19.8113 | 1050 | 0.0           | -               |
+| 20.7547 | 1100 | 0.0           | -               |
+| 21.6981 | 1150 | 0.0           | -               |
+| 22.6415 | 1200 | 0.0           | -               |
+| 23.5849 | 1250 | 0.0           | -               |
+| 24.5283 | 1300 | 0.0           | -               |
+| 25.4717 | 1350 | 0.0           | -               |
+| 26.4151 | 1400 | 0.0           | -               |
+| 27.3585 | 1450 | 0.0           | -               |
+| 28.3019 | 1500 | 0.0           | -               |
+| 29.2453 | 1550 | 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_fi",
+  "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

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+{
+  "__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

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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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+size 442494816

model_head.pkl

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "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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+    "lstrip": false,
+    "normalized": false,
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+  }
+}

tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}