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 +1,231 @@
+---
+base_model: mini1013/master_domain
+library_name: setfit
+metrics:
+- metric
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 코스트코 수지스 그릴드 닭가슴살 1.8kg 수비드 페퍼콘 허브 그릴드 닭가슴살 1.8kg (스테디) 리반태닝
+- text: 에쓰푸드 전지베이컨(1.9mm 슬라이스) 500g(기름기가 적고 담백한 베이컨)  금정푸드
+- text: 849967 동원 퀴진 통등심 돈까스 480g 3봉 외 4종 1)돈까스(통등심) 480g 1)돈까스(통등심) 480g_4)생선커틀렛
+    400g_4)생선커틀렛 400g 시드웰쓰파트너스
+- text: 돼지 뒷다리살 수육용 제육볶음고기 찌개용 ★핫딜대전★ 한돈 뒷다리살 1kg_보쌈용덩어리 주식회사 삼형제월드
+- text: 송이 불닭발 280gX10팩/국내산, 원앙, 닭발, 매운  (주)천지농산
+inference: true
+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: metric
+      value: 0.6435236614085759
+      name: Metric
+---
+
+# 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:** 8 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                                                                                                                                                                                                             |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 7.0   | <ul><li>'남도전통 우리맛 토종순대 천연돈장 1kg 4인분 우리맛 토종순대 1kg+1kg (2개) 주식회사 금호비앤디'</li><li>'코스트코 커클랜드 시그니춰 크럼블스 베이컨 567g  최고의수준'</li><li>'하림 아이로운 닭가슴살 팝콘치킨500g 1봉+1봉  팔레스티'</li></ul>                                            |
+| 2.0   | <ul><li>'청정원 안주야 매운곱창볶음 160g 4개  (주) 이카루스'</li><li>'삼치기 쫄여먹는 쫄갈비 300g 1-2인분 물갈비 캠핑요리 음식 밀키트 고기 양념돼지갈비 쫄여먹는 쫄갈비 300g(1~2인분) 삼치기'</li><li>'파티큐 귀족 통돼지바베큐 (5-10인분) 만화고기 캠핑음식 집들이 출장 부천종합버스터미널_1/6상체 주식회사 파티큐'</li></ul> |
+| 6.0   | <ul><li>'송화단(화풍60g x10) 8개 식자재 업소용 대용량  일흥상회'</li><li>'오리로스500gx4팩 고추오리불고기500gx1팩 선물용  마이다스'</li><li>'춘천달갈비 국내산 즉석조리식품 안동 순살 찜닭 1kg / 3-4인분  주식회사 에프앤에프커머스'</li></ul>                                                |
+| 0.0   | <ul><li>'Espuna 스페인 전통 하몽 초리초슬라이스100g1개jamon  밀도상점'</li><li>'목우촌 버터구이 치킨 봉 500gX2개  팔레스티몰'</li><li>'우리맛 모듬국밥 머리고기+내장 2인분 (440g) 모듬국밥 4pack (800g) 주식회사 금호비앤디'</li></ul>                                              |
+| 5.0   | <ul><li>'[호주산] 양등뼈 1kg  cj거성푸드'</li><li>'양의나라 유기농 양고기 양갈비 양꼬치 프렌치렉 숄더랙 캠핑 냉장 냉동  양의 나라'</li><li>'하이마블 프렌치랙 프랜치랙 ���갈비 양고기 450g 램 미니 토마호크 프렌치랙  450g (냉동) 주식회사 하이마블'</li></ul>                                          |
+| 1.0   | <ul><li>'하림 치킨너겟(Ⅱ) 1kg 텐더스틱 1kg 주식회사 미담'</li><li>'이종하작가 비법매실먹은 춘천닭갈비 올인원세트 3인분 (닭갈비 + 야채+떡+치즈 포함) 통다리살 간장바베큐 4개(1kg) 춘천맛식품'</li><li>'국물닭발 700g 2팩 튤립 숯불 오돌뼈 술안주 혼술 야식 국내산 매운맛 제육볶음 오돌뼈 250g 2팩 주식회사 바르'</li></ul>   |
+| 3.0   | <ul><li>'미트홀 부채살 찹스테이크 부채 큐브 스테이크 1kg(200gX5팩) 짜파구리  미트홀'</li><li>'[도착보장] 올반 소불고기 전골세트 (소불고기 4팩 + 전골육수 2팩) 저녁 국 탕 찌개 반찬 간편식 밀키트 소불고기 4팩+전골육수2팩 (주)신세계푸드'</li><li>'에스푸드 바싹 불고기 1kg  주식회사 클릭몰'</li></ul>               |
+| 4.0   | <ul><li>'흥생농장 반숙란40구 촉촉한 부드러운 반숙계란  흥생농장'</li><li>'에그트리 특란 90구 HACCP농장직송 날계란  에그트리농장'</li><li>'중국 염장 오리알 야단 372g 유황 찐오리알 6개입  오너트리'</li></ul>                                                                        |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.6435 |
+
+## 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_fd20")
+# Run inference
+preds = model("송이 불닭발 280gX10팩/국내산, 원앙, 닭발, 매운  (주)천지농산")
+```
+
+<!--
+### 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   | 10.0318 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 19                    |
+| 5.0   | 27                    |
+| 6.0   | 50                    |
+| 7.0   | 50                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (20, 20)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 40
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0182  | 1    | 0.4004        | -               |
+| 0.9091  | 50   | 0.238         | -               |
+| 1.8182  | 100  | 0.1002        | -               |
+| 2.7273  | 150  | 0.0799        | -               |
+| 3.6364  | 200  | 0.063         | -               |
+| 4.5455  | 250  | 0.0301        | -               |
+| 5.4545  | 300  | 0.0261        | -               |
+| 6.3636  | 350  | 0.0128        | -               |
+| 7.2727  | 400  | 0.0054        | -               |
+| 8.1818  | 450  | 0.008         | -               |
+| 9.0909  | 500  | 0.004         | -               |
+| 10.0    | 550  | 0.0001        | -               |
+| 10.9091 | 600  | 0.002         | -               |
+| 11.8182 | 650  | 0.002         | -               |
+| 12.7273 | 700  | 0.0058        | -               |
+| 13.6364 | 750  | 0.0039        | -               |
+| 14.5455 | 800  | 0.0016        | -               |
+| 15.4545 | 850  | 0.0001        | -               |
+| 16.3636 | 900  | 0.0001        | -               |
+| 17.2727 | 950  | 0.0001        | -               |
+| 18.1818 | 1000 | 0.0001        | -               |
+| 19.0909 | 1050 | 0.0           | -               |
+| 20.0    | 1100 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.46.1
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.20.0
+
+## 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_fd",
+  "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": "4.46.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.46.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1a65afbe7d1f8249469622898e970badc9af90268fc876f8e87fed9da480029
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79d41b56925556c60196a1e36e7f56df0cdccc7982780826f30b4f6f5ce1a909
+size 50087

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "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

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "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]"
+}