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mini1013
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master_cate_sl30

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +272 -0
  3. config.json +29 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: 요넥스 테니스공 홀더 메탈 볼클립 볼걸이 테니스용품 스포츠/레저>테니스>기타테니스용품
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+ - text: 스트링 스타팅 클램프 알루미늄 합금 익스텐션 코드 테니스 배드민턴 전문 액세서리 1m 스포츠/레저>테니스>기타테니스용품
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+ - text: 60 개 롤 스풀 10m 탄성 신축성 스트링 스레드 헤어 익스텐션 스레드 와이 스포츠/레저>테니스>스트링
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+ - text: 알로 MATCH POINT 여성 테니스 스커트 스포츠/레저>테니스>테니스의류
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+ - text: 디아도라 AIR TEX 테니스 볼 그래픽 반팔 티셔츠 GREEN D4221TRS14GNL 스포츠/레저>테니스>테니스의류
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: mini1013/master_domain
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+ model-index:
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+ - name: SetFit with mini1013/master_domain
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 1.0
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with mini1013/master_domain
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+
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+ 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 8 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 1.0 | <ul><li>'윌슨 테니스 진동 방지 2개들이 PROFEEL 프로필 WRZ537700 스포츠/레저>테니스>기타테니스용품'</li><li>'국제 단추엘보 대 2단 1개입 엘보링 테니스라켓 댐프너 스포츠/레저>테니스>기타테니스용품'</li><li>'테니스 라켓거치대 배드민턴채 진열대 수납 보관대 스포츠/레저>테니스>기타테니스용품'</li></ul> |
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+ | 5.0 | <ul><li>'윌슨 클래시 100 투어 테니스라켓 WR005711 스포츠/레저>테니스>테니스라켓'</li><li>'요넥스 아스트렐 100 테니스라켓 YY1209RT030 스포츠/레저>테니스>테니스라켓'</li><li>'낫소 옵티멈 투어 테니스라켓 스포츠/레저>테니스>테니스라켓'</li></ul> |
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+ | 2.0 | <ul><li>'슈퍼스트링 갓클래스 신의클래스 122 127 12M 스포츠/레저>테니스>스트링'</li><li>'낫소 다이너마이트 테니스 스트링 200M 스포츠/레저>테니스>스트링'</li><li>'LUXILON 럭실론 테니스 스트링 거트 롤 알루파워 러프 1 25 200m WRZ9902 스포츠/레저>테니스>스트링'</li></ul> |
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+ | 4.0 | <ul><li>'낫소스포츠 낫소 통볼 T-507C 스포츠/레저>테니스>테니스공'</li><li>'신신상사 스타스포츠 매치포인트 시합구 TB172 스포츠/레저>테니스>테니스공'</li><li>'프록시마 매치 포인트 테니스공 스포츠/레저>테니스>테니스공'</li></ul> |
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+ | 0.0 | <ul><li>'투나 GENUINE 가죽���립 1 교체용 쿠션그립 리플레이스먼트 테니스 원그립 스포츠/레저>테니스>그립'</li><li>'낫소 테니스 오버그립 30개입 NSOG-30 스포츠/레저>테니스>그립'</li><li>'감마 GAMMA Tennis Overgrip Ideal for Tennis Pickleball Squash Badminton and Racquetball Durable and 스포츠/레저>테니스>그립'</li></ul> |
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+ | 3.0 | <ul><li>'윌슨 WILSON 테니스 쉴드 슬링백 라켓 가방 스포츠/레저>테니스>테니스가방'</li><li>'부천정스포츠 라코스테 테니스가방 락팩 L23 스포츠 대용량 가방 스포츠/레저>테니스>테니스가방'</li><li>'윌슨 더플백 스포츠/레저>테니스>테니스가방'</li></ul> |
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+ | 7.0 | <ul><li>'아식스 젤리솔루션 9 올코트 1041A330 600 스포츠/레저>테니스>테니스화'</li><li>'아식스 COURT SLIDE 3 CLAY OC코트 슬라이드 OC 여성 테니스화 옴니 클레이용 신발 1042A230 220824ASTS 스포츠/레저>테니스>테니스화'</li><li>'아식스 코트 FF3 올코트 테니스화 여성 1042A220 400 스포츠/레저>테니스>테니스화'</li></ul> |
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+ | 6.0 | <ul><li>'윌슨 여성 윈 풀온 플리츠 테니스숏 반바지 클래식네이비 스포츠/레저>테니스>테니스의류'</li><li>'디아도라 테니스 라이프 그래픽 반팔티셔츠 VIOLET 스포츠/레저>테니스>테니스의류'</li><li>'라코스테 스포츠 치마바지 테니스 베이직 플리츠 스커트 7WJ JF0990-54G 스포츠/레저>테니스>테니스의류'</li></ul> |
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+
74
+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 1.0 |
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+
81
+ ## Uses
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+
83
+ ### Direct Use for Inference
84
+
85
+ First install the SetFit library:
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+
87
+ ```bash
88
+ pip install setfit
89
+ ```
90
+
91
+ Then you can load this model and run inference.
92
+
93
+ ```python
94
+ from setfit import SetFitModel
95
+
96
+ # Download from the 🤗 Hub
97
+ model = SetFitModel.from_pretrained("mini1013/master_cate_sl30")
98
+ # Run inference
99
+ preds = model("알로 MATCH POINT 여성 테니스 스커트 스포츠/레저>테니스>테니스의류")
100
+ ```
101
+
102
+ <!--
103
+ ### Downstream Use
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+
105
+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
108
+ <!--
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+ ### Out-of-Scope Use
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+
111
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
113
+
114
+ <!--
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+ ## Bias, Risks and Limitations
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+
117
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
120
+ <!--
121
+ ### Recommendations
122
+
123
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
124
+ -->
125
+
126
+ ## Training Details
127
+
128
+ ### Training Set Metrics
129
+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 3 | 8.2241 | 18 |
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+
133
+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0.0 | 70 |
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+ | 1.0 | 70 |
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+ | 2.0 | 70 |
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+ | 3.0 | 70 |
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+ | 4.0 | 50 |
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+ | 5.0 | 70 |
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+ | 6.0 | 70 |
142
+ | 7.0 | 70 |
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+
144
+ ### Training Hyperparameters
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+ - batch_size: (256, 256)
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+ - num_epochs: (30, 30)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 50
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
154
+ - margin: 0.25
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+ - end_to_end: False
156
+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:----:|:-------------:|:---------------:|
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+ | 0.0094 | 1 | 0.4693 | - |
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+ | 0.4717 | 50 | 0.4966 | - |
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+ | 0.9434 | 100 | 0.2749 | - |
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+ | 1.4151 | 150 | 0.0397 | - |
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+ | 1.8868 | 200 | 0.0179 | - |
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+ | 2.3585 | 250 | 0.0076 | - |
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+ | 2.8302 | 300 | 0.0 | - |
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+ | 3.3019 | 350 | 0.0 | - |
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+ | 3.7736 | 400 | 0.0 | - |
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+ | 4.2453 | 450 | 0.0 | - |
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+ | 4.7170 | 500 | 0.0 | - |
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+ | 5.1887 | 550 | 0.0 | - |
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+ | 5.6604 | 600 | 0.0 | - |
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+ | 6.1321 | 650 | 0.0 | - |
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+ | 6.6038 | 700 | 0.0 | - |
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+ | 7.0755 | 750 | 0.0 | - |
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+ | 7.5472 | 800 | 0.0 | - |
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+ | 8.0189 | 850 | 0.0 | - |
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+ | 8.4906 | 900 | 0.0 | - |
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+ | 8.9623 | 950 | 0.0 | - |
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+ | 9.4340 | 1000 | 0.0 | - |
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+ | 9.9057 | 1050 | 0.0 | - |
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+ | 10.3774 | 1100 | 0.0 | - |
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+ | 10.8491 | 1150 | 0.0 | - |
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+ | 11.3208 | 1200 | 0.0 | - |
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+ | 11.7925 | 1250 | 0.0 | - |
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+ | 12.2642 | 1300 | 0.0 | - |
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+ | 12.7358 | 1350 | 0.0 | - |
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+ | 13.2075 | 1400 | 0.0 | - |
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+ | 13.6792 | 1450 | 0.0 | - |
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+ | 14.1509 | 1500 | 0.0 | - |
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+ | 14.6226 | 1550 | 0.0 | - |
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+ | 15.0943 | 1600 | 0.0 | - |
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+ | 15.5660 | 1650 | 0.0 | - |
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+ | 16.0377 | 1700 | 0.0 | - |
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+ | 16.5094 | 1750 | 0.0 | - |
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+ | 16.9811 | 1800 | 0.0 | - |
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+ | 17.4528 | 1850 | 0.0 | - |
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+ | 17.9245 | 1900 | 0.0 | - |
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+ | 18.3962 | 1950 | 0.0 | - |
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+ | 18.8679 | 2000 | 0.0 | - |
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+ | 19.3396 | 2050 | 0.0 | - |
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+ | 19.8113 | 2100 | 0.0 | - |
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+ | 20.2830 | 2150 | 0.0 | - |
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+ | 20.7547 | 2200 | 0.0 | - |
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+ | 21.2264 | 2250 | 0.0 | - |
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+ | 21.6981 | 2300 | 0.0 | - |
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+ | 22.1698 | 2350 | 0.0 | - |
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+ | 22.6415 | 2400 | 0.0 | - |
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+ | 23.1132 | 2450 | 0.0 | - |
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+ | 23.5849 | 2500 | 0.0 | - |
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+ | 24.0566 | 2550 | 0.0 | - |
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+ | 24.5283 | 2600 | 0.0 | - |
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+ | 25.0 | 2650 | 0.0 | - |
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+ | 25.4717 | 2700 | 0.0 | - |
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+ | 25.9434 | 2750 | 0.0 | - |
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+ | 26.4151 | 2800 | 0.0 | - |
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+ | 26.8868 | 2850 | 0.0 | - |
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+ | 27.3585 | 2900 | 0.0 | - |
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+ | 27.8302 | 2950 | 0.0 | - |
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+ | 28.3019 | 3000 | 0.0 | - |
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+ | 28.7736 | 3050 | 0.0 | - |
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+ | 29.2453 | 3100 | 0.0 | - |
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+ | 29.7170 | 3150 | 0.0 | - |
230
+
231
+ ### Framework Versions
232
+ - Python: 3.10.12
233
+ - SetFit: 1.1.0
234
+ - Sentence Transformers: 3.3.1
235
+ - Transformers: 4.44.2
236
+ - PyTorch: 2.2.0a0+81ea7a4
237
+ - Datasets: 3.2.0
238
+ - Tokenizers: 0.19.1
239
+
240
+ ## Citation
241
+
242
+ ### BibTeX
243
+ ```bibtex
244
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
245
+ doi = {10.48550/ARXIV.2209.11055},
246
+ url = {https://arxiv.org/abs/2209.11055},
247
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
249
+ title = {Efficient Few-Shot Learning Without Prompts},
250
+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
253
+ }
254
+ ```
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+
256
+ <!--
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+ ## Glossary
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+
259
+ *Clearly define terms in order to be accessible across audiences.*
260
+ -->
261
+
262
+ <!--
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+ ## Model Card Authors
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+
265
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_name_or_path": "mini1013/master_item_sl_org_gtcate",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
15
+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
17
+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
19
+ "num_attention_heads": 12,
20
+ "num_hidden_layers": 12,
21
+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
23
+ "tokenizer_class": "BertTokenizer",
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+ "torch_dtype": "float32",
25
+ "transformers_version": "4.44.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "__version__": {
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+ "sentence_transformers": "3.3.1",
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+ "transformers": "4.44.2",
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+ "pytorch": "2.2.0a0+81ea7a4"
6
+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": "cosine"
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+ }
config_setfit.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ {
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+ "labels": null,
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+ "normalize_embeddings": false
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+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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