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 +1,233 @@
+---
+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: (시흥점)루이까또즈 여성 3단 반지갑 SP3HT03IV 아이보리_ONE SIZE 신세계프리미엄아울렛
+- text: 닥스 악세서리 남성 22FW populet 로고패턴 소가죽 반지갑 WBWA2F729BK  정품(Best Quality)스토어
+- text: '베노베로 (23FW) 알렉스 소프트 엠보 소가죽 미니중지갑 BJF1ACP1201K1-BS 블랙(선물아님) '
+- text: '[갤러리아] [헤지스ACC] HIHO2F602G2 [LEENA] 그레이 배색 가죽 목걸이카드홀더(한화갤러리아㈜ 센터시티)  한화갤러리아(주)'
+- text: '[롯데백화점]라코스테 24SS (여성) 데일리 라이프스타일 지퍼 반지갑 [NF4375D54G 000 YDP]  롯데백화점_'
+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: 0.7924514420247204
+      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                                                                                                                                                                                                                                              |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'해킹방지 카본 카드지갑 RFID 도난방지 자석오토지갑 블랙 화인트레이드'</li><li>'[라코스테](천안아산점)더 블렌드 포켓 오거나이저(NH4134L54GH45)  신세계백화점'</li><li>'닥스_핸드백 (선물포장)(DAKS X DISNEY) 미키마우스 가죽배색 체크 여성 카드  롯데백화점2관'</li></ul>                                                          |
+| 1.0   | <ul><li>'이케아 KNOLIG 크뇔리그 동전지갑 소품 가방 주머니 참 인테리어 색상_옐로우 호랑이스토어5'</li><li>'레오파드 미니 동전지갑 캐리어파우치  폰토스(Pontos)'</li><li>'[비비안웨스트우드][비비안 웨스트우드] 조르단 더블 프레임 동전지갑 52020041 L001J N403(김해점) ONE SIZE 신세계백화점'</li></ul>                                          |
+| 5.0   | <ul><li>'BEANPOLE] 빈폴 ACC 스트랩 파우치/카드 SET 블랙/핑크(BE04A4W995) 블랙 메가 세일'</li><li>'지갑& 벨트01G1295Z8K외5종/피에르가르뎅_핸드백 01G1295Z8K 롯데쇼핑(주)'</li><li>'[빈폴 ACC] 스트랩 파우치/카드 SET 블랙 (BE04A4W995) 블랙_one size 윈아이'</li></ul>                                          |
+| 4.0   | <ul><li>'[헤지스ACC]HJHO3F332W2/[23FW] 브라운 로고패턴 가죽 키링  에이케이에스앤디 (주) AK인터넷쇼핑몰'</li><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉] 블랙 로고패턴 가죽 키링 DBHO4E138  롯데백화점_'</li><li>'[선물포장] HJHO3E281BK_남성 블랙 퍼피로고 체크배색 키링/헤지스ACC  롯데쇼핑(주)'</li></ul>                              |
+| 0.0   | <ul><li>'타미힐피거 타미힐피거 남성반지갑 31TL22X046 블랙 네이비 네이비 SK스토아모바일'</li><li>'[선물포장] DBWA3F717W3 브라운 악어가죽/닥스ACC  롯데쇼핑(주)'</li><li>'[헤지스 액세서리] [24SS] HJWA4E906BK Online 한정판BASIC 블랙 솔리드 퍼피로고 소 XXX '</li></ul>                                                  |
+| 3.0   | <ul><li>'여성반지갑 SL3AL04BL/루이까또즈 BLACK 롯데쇼핑(주)'</li><li>'MINI POCKET - BLACK  주식회사 이코컴퍼니'</li><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉]브라운 체크 가죽 핸드폰케이스 DCHO2F328W2  롯데백화점_'</li></ul>                                                                             |
+| 7.0   | <ul><li>'동지갑 베트남 환전 통장 여행 슬림 파우치 다낭 해외 지퍼 여권 03. 블랙 동쯔몰'</li><li>'도장 가방 인감 스탬프 케이스 수납 문서 보관 통장 V번 인감 수납가방 홍마켓(hong)'</li><li>'여행용 여권 파우치 목걸이 수납 휴대용 보호커버 블루 나이스쇼핑'</li></ul>                                                                          |
+| 2.0   | <ul><li>'[갤러리아] 8059461 MS CHASE GC9 B2871 ONE SIZE 한화갤러리아(주)'</li><li>'국내발송 MATIN KIM 마땡킴 GLOSSY CAMP WALLET IN WHITE MK2311WL001M0WH FREE 말로스'</li><li>'[헤지스](신세계본점)[HAZZYS ACC] [GOLDEN LANE] 블랙 로고패턴 소가죽 반지갑 HJWA1F562BK  주식회사 에스에스지닷컴'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.7925 |
+
+## 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_ac14")
+# Run inference
+preds = model("(시흥점)루이까또즈 여성 3단 반지갑 SP3HT03IV 아이보리_ONE SIZE 신세계프리미엄아울렛")
+```
+
+<!--
+### 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   | 9.21   | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 50                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0159  | 1    | 0.3853        | -               |
+| 0.7937  | 50   | 0.2743        | -               |
+| 1.5873  | 100  | 0.1039        | -               |
+| 2.3810  | 150  | 0.0564        | -               |
+| 3.1746  | 200  | 0.0306        | -               |
+| 3.9683  | 250  | 0.0124        | -               |
+| 4.7619  | 300  | 0.0146        | -               |
+| 5.5556  | 350  | 0.0008        | -               |
+| 6.3492  | 400  | 0.0007        | -               |
+| 7.1429  | 450  | 0.0001        | -               |
+| 7.9365  | 500  | 0.0001        | -               |
+| 8.7302  | 550  | 0.0001        | -               |
+| 9.5238  | 600  | 0.0001        | -               |
+| 10.3175 | 650  | 0.0001        | -               |
+| 11.1111 | 700  | 0.0001        | -               |
+| 11.9048 | 750  | 0.0001        | -               |
+| 12.6984 | 800  | 0.0001        | -               |
+| 13.4921 | 850  | 0.0001        | -               |
+| 14.2857 | 900  | 0.0001        | -               |
+| 15.0794 | 950  | 0.0           | -               |
+| 15.8730 | 1000 | 0.0001        | -               |
+| 16.6667 | 1050 | 0.0           | -               |
+| 17.4603 | 1100 | 0.0           | -               |
+| 18.2540 | 1150 | 0.0           | -               |
+| 19.0476 | 1200 | 0.0           | -               |
+| 19.8413 | 1250 | 0.0           | -               |
+
+### 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_ac",
+  "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 sha256:9c64042b482fffada65f1aa1901942a492b4f352a20781b246a60ee4687f6acf
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbde64de8fd1005e8d42d2911ea129b5abacf84c2d3ff26387af3e9b93c414dc
+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]"
+}