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 +1,250 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 돌반지 백일 호랑이 호랑이띠 목걸이 3.75g 토퍼없음_14k 아기목걸이 기본3푼줄 옐로우골드 출산/육아 > 유아동주얼리 > 순금돌반지
+- text: '[국제금거래소] (순도99.9%) 고급 순금 돌반지 1.875g 복(福)_고급케이스 출산/육아 > 유아동주얼리 > 순금돌반지'
+- text: 베블링 순금 아기 돌팔찌 첫돌 백일 돌선물 3.75g 5.625g 7.5g 11.25g 3.75g_02.Happy 100 Days_국문
+    가는체 출산/육아 > 유아동주얼리 > 순금주얼리
+- text: 별별나라 24k 순금 돌반지 조카 첫돌 아기백일 선물 3.75g 행운의 토끼 돌반지_기본 케이스 출산/육아 > 유아동주얼리 > 순금돌반지
+- text: 바 탄생석 미아방지 실버세트/ 목걸이/ 팔찌 바 탄생석 미아방지 실버목걸이_1월(Garnet)_I타입(영문 우아체) 출산/육아 > 유아동주얼리
+    > 주얼리세트
+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>'핸드메이드 어린이 귀걸이 안아픈 귀찌 실리콘 나사형 진주볼-화이트 출산/육아 > 유아동주얼리 > 귀걸이'</li><li>'초등학생 어린이 귀걸이 귀찌 클립형 나사형 퍼플 큐빅_클립형 귀찌(무니켈) 출산/육아 > 유아동주얼리 > 귀걸이'</li><li>'어린이 초등학생 여아 귀걸이 귀찌 e513-1.미니하트퍼플 드롭_귀찌 출산/육아 > 유아동주얼리 > 귀걸이'</li></ul>                                                                                                                              |
+| 4.0   | <ul><li>'미니써클 미아방지 실버세트/ 목걸이/ 팔찌 미니써클 미아방지 실버세트_J타입(영문 흘림체)_F타입(영문 바른체) 출산/육아 > 유아동주얼리 > 주얼리세트'</li><li>'겨울왕국2 엘사 눈꽃 목걸이 팔찌 반지 3종세트  출산/육아 > 유아동주얼리 > 주얼리세트'</li><li>'스키니 네모 탄생석 미아방지 실버세트/ 목걸이/ 팔찌 스키니 네모 미아방지 실버세트_1월(Garnet)_H타입(영문 볼드체) 출산/육아 > 유아동주얼리 > 주얼리세트'</li></ul>                                                                             |
+| 2.0   | <ul><li>'리바스골드 99.9 순금 1g/1.875g/3.75g 돌반지 금수저 순금열쇠 돌팔찌 모음전 09. 돌반지(왕관하트)_1.875g_H. 금박 폴라로이드 액자 패키지 출산/육아 > 유아동주얼리 > 순금돌반지'</li><li>'컷팅 왕관 돌반지 돌팔찌 3.75g 7.5g 11.25g 순금 자개함 보자기포장 백일반지 컷팅 왕관 돌반지 3.75g_백일 남색 리본_고급와당케이스 출산/육아 > 유아동주얼리 > 순금돌반지'</li><li>'호랑이 토끼 띠 밴드 돌반지 3.75g 순금 자개함 아기 백일반지 리본 토끼 3.75g_빨강 수국_안음기본케이스 출산/육아 > 유아동주얼리 > 순금돌반지'</li></ul> |
+| 5.0   | <ul><li>'민트 핑크 하트볼체인 실버 아기미아방지팔찌 3mm볼_빨강＋각인함★_14＋2 （5-7세） 출산/육아 > 유아동주얼리 > 팔찌'</li><li>'스틱 바 미아방지 목걸이 팔찌 아기 아이 어린이 유아동 실버 순은 팔찌로 제작해주세요_탄생석 2월 출산/육아 > 유아동주얼리 > 팔찌'</li><li>'미아방지팔찌(14K 18K 발도장 돌팔찌 돌선물 백일선물 탄생석) 1860B 3번 고딕체_10월 핑크투어마린_18K / 사다리체인 출산/육아 > 유아동주얼리 > 팔찌'</li></ul>                                                                      |
+| 3.0   | <ul><li>'순금 돌팔찌 3.75g 7.5g 11.25g 8종 모음전 24k 첫돌 백일 아기 조카 선물 ⑶11.25g_⑴천사날개 출산/육아 > 유아동주얼리 > 순금주얼리'</li><li>'순금 당근 토끼띠 아기 돌목걸이 백일목걸이 3.75g 7.5g (99.9%) 순금 당근 토끼띠 아기목걸이/7.5g_색동 복주머니 팩킹_여자아기 출산/육아 > 유아동주얼리 > 순금주얼리'</li><li>'금수저 책연필 돌팔찌 7.5g 11.25g 18.75g 순금 자개함 포장 백일팔찌 금수저7.5g_첫돌 빨강 리본_고급송학자개케이스 출산/육아 > 유아동주얼리 > 순금주얼리'</li></ul>                    |
+| 1.0   | <ul><li>'하트 데이지 14k 미아방지목걸이 18k 아기 금목걸이 여아 딸  출산/육아 > 유아동주얼리 > 목걸이/펜던트'</li><li>'실버 미아방지 목걸이 하트 탄생석 아기 돌선물 순은925 어린이날 기념 각인 조카선물  출산/육아 > 유아동주얼리 > 목걸이/펜던트'</li><li>'실버 오목코인 키즈 아기목걸이 미아방지목걸이  출산/육아 > 유아동주얼리 > 목걸이/펜던트'</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_bc21")
+# Run inference
+preds = model("[국제금거래소] (순도99.9%) 고급 순금 돌반지 1.875g 복(福)_고급케이스 출산/육아 > 유아동주얼리 > 순금돌반지")
+```
+
+<!--
+### 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   | 15.7703 | 32  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 20                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0137  | 1    | 0.4867        | -               |
+| 0.6849  | 50   | 0.4987        | -               |
+| 1.3699  | 100  | 0.3808        | -               |
+| 2.0548  | 150  | 0.1425        | -               |
+| 2.7397  | 200  | 0.053         | -               |
+| 3.4247  | 250  | 0.0037        | -               |
+| 4.1096  | 300  | 0.0001        | -               |
+| 4.7945  | 350  | 0.0001        | -               |
+| 5.4795  | 400  | 0.0001        | -               |
+| 6.1644  | 450  | 0.0001        | -               |
+| 6.8493  | 500  | 0.0           | -               |
+| 7.5342  | 550  | 0.0           | -               |
+| 8.2192  | 600  | 0.0           | -               |
+| 8.9041  | 650  | 0.0           | -               |
+| 9.5890  | 700  | 0.0           | -               |
+| 10.2740 | 750  | 0.0           | -               |
+| 10.9589 | 800  | 0.0           | -               |
+| 11.6438 | 850  | 0.0           | -               |
+| 12.3288 | 900  | 0.0           | -               |
+| 13.0137 | 950  | 0.0           | -               |
+| 13.6986 | 1000 | 0.0           | -               |
+| 14.3836 | 1050 | 0.0           | -               |
+| 15.0685 | 1100 | 0.0           | -               |
+| 15.7534 | 1150 | 0.0           | -               |
+| 16.4384 | 1200 | 0.0           | -               |
+| 17.1233 | 1250 | 0.0           | -               |
+| 17.8082 | 1300 | 0.0           | -               |
+| 18.4932 | 1350 | 0.0           | -               |
+| 19.1781 | 1400 | 0.0           | -               |
+| 19.8630 | 1450 | 0.0           | -               |
+| 20.5479 | 1500 | 0.0           | -               |
+| 21.2329 | 1550 | 0.0           | -               |
+| 21.9178 | 1600 | 0.0           | -               |
+| 22.6027 | 1650 | 0.0           | -               |
+| 23.2877 | 1700 | 0.0           | -               |
+| 23.9726 | 1750 | 0.0           | -               |
+| 24.6575 | 1800 | 0.0           | -               |
+| 25.3425 | 1850 | 0.0           | -               |
+| 26.0274 | 1900 | 0.0           | -               |
+| 26.7123 | 1950 | 0.0           | -               |
+| 27.3973 | 2000 | 0.0           | -               |
+| 28.0822 | 2050 | 0.0           | -               |
+| 28.7671 | 2100 | 0.0           | -               |
+| 29.4521 | 2150 | 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_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

@@ -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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+oid sha256:fb68a49a4c366fa2fe7de5d71565380760452bc22ae1cf4cd64f95460537b495
+size 442494816

model_head.pkl

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+oid sha256:d6184a83ea14aa583be94c47eafc6d4630a00fd596054008994081b3545915f6
+size 37767

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
+  },
+  "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

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