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 +1,298 @@
+---
+base_model: klue/roberta-base
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 밀크바오밥 오리지널 샴푸 베이비파우더 1L 09_트리트먼트 화이트머스크 1000ml (#M)화장품/미용>헤어케어>샴푸 AD > Naverstore
+    > 화장품/미용 > 헤어케어 > 샴푸 > 약산성샴푸
+- text: 무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어
+    > 바디케어 > 바디케어세트
+- text: 1+1세트~(컨센+릴렉스마스크100ml) 에스테티카 데미지 케어 컨센트레이트 120ml (열활성 열보호 에센스) 정품 + 릴렉스마스크100ml
+    1개 (#M)쿠팡 홈>싱글라이프>샤워/세안>헤어에센스 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어에센스
+- text: 헤드스파7 트리트먼트 더 프리미엄 210ml + 210ml MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트 Gmarket
+    > 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트
+- text: 헤어플러스 실크 코팅 트리트먼트 50ml 4개 실크 코팅 트리트먼트 50ml 4개 위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어
+    > 트리트먼트;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어;위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 >
+    샴푸/린스;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어
+    > 트리트먼트
+inference: true
+model-index:
+- name: SetFit with klue/roberta-base
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8206115779645191
+      name: Accuracy
+---
+
+# SetFit with klue/roberta-base
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
+- **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:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'로레알파리 토탈리페어5 트리트먼트 헤어팩 170ml × 1개 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li><li>'아모스 녹차실감  인텐시브 팩 250ml 녹차실감 인텐시브팩250g 홈>전체상품;(#M)홈>녹차실감 Naverstore > 화장품/미용 > 헤어케어 > 헤어팩'</li><li>'프리미엄 헤어클리닉 헤어팩 258ml 베이���파우더 LotteOn > 뷰티 > 헤어케어 > 헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li></ul>                                                                                                                   |
+| 0     | <ul><li>'퓨어시카 트리트먼트 베이비파우더향 1000ml 1개 MinSellAmount 스마일배송 홈>뷰티>바디케어>바디워시;스마일배송 홈>;(#M)스마일배송 홈>뷰티>헤어케어/스타일링>트리트먼트/팩 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디클렌저'</li><li>'1+1 살림백서 탈모 샴푸 엑티브B7 맥주효모 앤 비오틴 1000ml 남자 여자 바이오틴 4)오푼티아 트리트먼트 유칼립투스 1L (#M)화장품/미용>헤어케어>탈모케어 AD > Naverstore > 화장품/미용 > 가을뷰티 > 각질관리템 > 탈모샴푸'</li><li>'1+1 살림백서 오푼티아 퍼퓸 샴푸 500ml 약산성 비듬 지성 두피 볼륨 유칼립투스향 13.유칼립투스 트리트먼트 1+1 500ml (#M)화장품/미용>헤어케어>샴푸 AD > Naverstore > 화장품/미용 > 머스크 > 샴푸'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8206   |
+
+## 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_top_bt13_9")
+# Run inference
+preds = model("무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디케어세트")
+```
+
+<!--
+### 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   | 14  | 23.76  | 98  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 50                    |
+
+### Training Hyperparameters
+- batch_size: (64, 64)
+- num_epochs: (30, 30)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 100
+- 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0064  | 1    | 0.4326        | -               |
+| 0.3185  | 50   | 0.3579        | -               |
+| 0.6369  | 100  | 0.2616        | -               |
+| 0.9554  | 150  | 0.0326        | -               |
+| 1.2739  | 200  | 0.0           | -               |
+| 1.5924  | 250  | 0.0           | -               |
+| 1.9108  | 300  | 0.0           | -               |
+| 2.2293  | 350  | 0.0           | -               |
+| 2.5478  | 400  | 0.0           | -               |
+| 2.8662  | 450  | 0.0           | -               |
+| 3.1847  | 500  | 0.0           | -               |
+| 3.5032  | 550  | 0.0           | -               |
+| 3.8217  | 600  | 0.0           | -               |
+| 4.1401  | 650  | 0.0           | -               |
+| 4.4586  | 700  | 0.0           | -               |
+| 4.7771  | 750  | 0.0           | -               |
+| 5.0955  | 800  | 0.0           | -               |
+| 5.4140  | 850  | 0.0           | -               |
+| 5.7325  | 900  | 0.0           | -               |
+| 6.0510  | 950  | 0.0           | -               |
+| 6.3694  | 1000 | 0.0           | -               |
+| 6.6879  | 1050 | 0.0           | -               |
+| 7.0064  | 1100 | 0.0           | -               |
+| 7.3248  | 1150 | 0.0           | -               |
+| 7.6433  | 1200 | 0.0           | -               |
+| 7.9618  | 1250 | 0.0           | -               |
+| 8.2803  | 1300 | 0.0           | -               |
+| 8.5987  | 1350 | 0.0           | -               |
+| 8.9172  | 1400 | 0.0           | -               |
+| 9.2357  | 1450 | 0.0           | -               |
+| 9.5541  | 1500 | 0.0           | -               |
+| 9.8726  | 1550 | 0.0           | -               |
+| 10.1911 | 1600 | 0.0           | -               |
+| 10.5096 | 1650 | 0.0           | -               |
+| 10.8280 | 1700 | 0.0           | -               |
+| 11.1465 | 1750 | 0.0           | -               |
+| 11.4650 | 1800 | 0.0           | -               |
+| 11.7834 | 1850 | 0.0           | -               |
+| 12.1019 | 1900 | 0.0           | -               |
+| 12.4204 | 1950 | 0.0           | -               |
+| 12.7389 | 2000 | 0.0           | -               |
+| 13.0573 | 2050 | 0.0           | -               |
+| 13.3758 | 2100 | 0.0           | -               |
+| 13.6943 | 2150 | 0.0           | -               |
+| 14.0127 | 2200 | 0.0           | -               |
+| 14.3312 | 2250 | 0.0           | -               |
+| 14.6497 | 2300 | 0.0           | -               |
+| 14.9682 | 2350 | 0.0           | -               |
+| 15.2866 | 2400 | 0.0           | -               |
+| 15.6051 | 2450 | 0.0           | -               |
+| 15.9236 | 2500 | 0.0           | -               |
+| 16.2420 | 2550 | 0.0           | -               |
+| 16.5605 | 2600 | 0.0           | -               |
+| 16.8790 | 2650 | 0.0           | -               |
+| 17.1975 | 2700 | 0.0           | -               |
+| 17.5159 | 2750 | 0.0           | -               |
+| 17.8344 | 2800 | 0.0           | -               |
+| 18.1529 | 2850 | 0.0           | -               |
+| 18.4713 | 2900 | 0.0           | -               |
+| 18.7898 | 2950 | 0.0           | -               |
+| 19.1083 | 3000 | 0.0           | -               |
+| 19.4268 | 3050 | 0.0           | -               |
+| 19.7452 | 3100 | 0.0           | -               |
+| 20.0637 | 3150 | 0.0           | -               |
+| 20.3822 | 3200 | 0.0           | -               |
+| 20.7006 | 3250 | 0.0           | -               |
+| 21.0191 | 3300 | 0.0           | -               |
+| 21.3376 | 3350 | 0.0           | -               |
+| 21.6561 | 3400 | 0.0           | -               |
+| 21.9745 | 3450 | 0.0           | -               |
+| 22.2930 | 3500 | 0.0           | -               |
+| 22.6115 | 3550 | 0.0           | -               |
+| 22.9299 | 3600 | 0.0           | -               |
+| 23.2484 | 3650 | 0.0           | -               |
+| 23.5669 | 3700 | 0.0           | -               |
+| 23.8854 | 3750 | 0.0           | -               |
+| 24.2038 | 3800 | 0.0           | -               |
+| 24.5223 | 3850 | 0.0           | -               |
+| 24.8408 | 3900 | 0.0           | -               |
+| 25.1592 | 3950 | 0.0           | -               |
+| 25.4777 | 4000 | 0.0           | -               |
+| 25.7962 | 4050 | 0.0           | -               |
+| 26.1146 | 4100 | 0.0           | -               |
+| 26.4331 | 4150 | 0.0           | -               |
+| 26.7516 | 4200 | 0.0           | -               |
+| 27.0701 | 4250 | 0.0           | -               |
+| 27.3885 | 4300 | 0.0           | -               |
+| 27.7070 | 4350 | 0.0           | -               |
+| 28.0255 | 4400 | 0.0           | -               |
+| 28.3439 | 4450 | 0.0           | -               |
+| 28.6624 | 4500 | 0.0           | -               |
+| 28.9809 | 4550 | 0.0           | -               |
+| 29.2994 | 4600 | 0.0           | -               |
+| 29.6178 | 4650 | 0.0           | -               |
+| 29.9363 | 4700 | 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_domain",
+  "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

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

model.safetensors

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+size 442494816

model_head.pkl

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modules.json

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+[
+  {
+    "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

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "single_word": false
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+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "lstrip": false,
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+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+      "single_word": false,
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+  },
+  "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]"
+}