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

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '[매일유업]요미요미 유기농 쌀떡뻥 시금치와브로콜리30g 한팩골라담기 쌀과자 초록1단계(6개월 이후) 25g 출산/육아 > 아기간식
+    > 유아과자'
+- text: 팔도 뽀로로 밀크맛 235ml 1개입 대페트_칠성 제로 사이다 1.5L 12개입 출산/육아 > 아기간식 > 유아음료
+- text: '[맛있는풍경] 유기농 쌀스틱 떡뻥 요거트볼 유아과자 어린이간식 11. 유기농 요거트 블루베리 출산/육아 > 아기간식 > 유아과자'
+- text: 팔도 뽀로로 딸기맛 235ml 12개 팩음료_JARDIN 로얄 헤이즐넛 230ml 20개 출산/육아 > 아기간식 > 유아음료
+- text: '[베베당] 유기농 아기과자 아이 간식 떡뻥 쌀과자 롱 스틱 팝 요거트 치즈 과일칩 10+3 10. 유기농 현미스틱 브로콜리30g 출산/육아
+    > 아기간식 > 유아과자'
+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: 0.9978471474703983
+      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:** 3 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                                                                                                                                                                                                                                                                          |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'팔도 뽀로로 홍삼쏙쏙 오렌지 100ml 20포  출산/육아 > 아기간식 > 유아음료'</li><li>'팔도 뽀로로 음료 어린이 키즈 주스 식혜 홍삼쏙쏙 워터젤리 모음 2.페트음료_사과x12개+블루베리12개 출산/육아 > 아기간식 > 유아음료'</li><li>'[1+1] 학교로 간 어린이주스 아기음료 유아주스 11종 사과즙 배도라지즙 [일반캡] 샤인머스캣 20팩_★안전캡★ 배 20팩_학교로간 10팩(맛&캡타입 랜덤) 출산/육아 > 아기간식 > 유아음료'</li></ul> |
+| 0.0   | <ul><li>'[산골이유식] 산골간식 쌀참 떡뻥 과일참 알밤 꿀밤 배도라지즙 퓨레 푸딩 요거트 비타민젤리 어린이김 쌈장 사과퓨레1팩 출산/육아 > 아기간식 > 유아과자'</li><li>'내아이애 아기과자 유기농 떡뻥 백미 08_유기농 떡뻥 양파 출산/육아 > 아기간식 > 유아과자'</li><li>'숲바른 유기농 맑음과자 국내산 아기 과자 유아 간식 떡뻥 스틱 [스틱]단호박 출산/육아 > 아기간식 > 유아과자'</li></ul>                                 |
+| 1.0   | <ul><li>'파스퇴르 위드맘 산양 제왕 100일 유아이유식분유 750g × 3개 출산/육아 > 아기간식 > 유아유제품'</li><li>'앱솔루트 킨더밀쉬 200ml  출산/육아 > 아기간식 > 유아유제품'</li><li>'매일유업 상하치즈 유기농 어린이치즈 3단계 60매 아기 간식  출산/육아 > 아기간식 > 유아유제품'</li></ul>                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9978   |
+
+## 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_bc12")
+# Run inference
+preds = model("팔도 뽀로로 밀크맛 235ml 1개입 대페트_칠성 제로 사이다 1.5L 12개입 출산/육아 > 아기간식 > 유아음료")
+```
+
+<!--
+### 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   | 7   | 15.3381 | 37  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0238  | 1    | 0.4944        | -               |
+| 1.1905  | 50   | 0.4153        | -               |
+| 2.3810  | 100  | 0.1469        | -               |
+| 3.5714  | 150  | 0.0014        | -               |
+| 4.7619  | 200  | 0.0001        | -               |
+| 5.9524  | 250  | 0.0001        | -               |
+| 7.1429  | 300  | 0.0001        | -               |
+| 8.3333  | 350  | 0.0           | -               |
+| 9.5238  | 400  | 0.0           | -               |
+| 10.7143 | 450  | 0.0           | -               |
+| 11.9048 | 500  | 0.0           | -               |
+| 13.0952 | 550  | 0.0           | -               |
+| 14.2857 | 600  | 0.0           | -               |
+| 15.4762 | 650  | 0.0           | -               |
+| 16.6667 | 700  | 0.0           | -               |
+| 17.8571 | 750  | 0.0           | -               |
+| 19.0476 | 800  | 0.0           | -               |
+| 20.2381 | 850  | 0.0           | -               |
+| 21.4286 | 900  | 0.0           | -               |
+| 22.6190 | 950  | 0.0           | -               |
+| 23.8095 | 1000 | 0.0           | -               |
+| 25.0    | 1050 | 0.0           | -               |
+| 26.1905 | 1100 | 0.0           | -               |
+| 27.3810 | 1150 | 0.0           | -               |
+| 28.5714 | 1200 | 0.0           | -               |
+| 29.7619 | 1250 | 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_bc",
+  "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
+}

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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+    "lstrip": false,
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+}

tokenizer_config.json

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+}