Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +10 -0
- README.md +208 -0
- config.json +29 -0
- config_sentence_transformers.json +10 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +66 -0
- vocab.txt +0 -0
1_Pooling/config.json
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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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}
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README.md
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---
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base_model: mini1013/master_domain
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library_name: setfit
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metrics:
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- metric
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pipeline_tag: text-classification
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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: SD 바이오 에스디 코드프리 당뇨검사지 4박스 200매 (유효기간 2025년 03월) 코드프리 200매+알콜솜 100매 엠에스메디칼
|
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- text: 아큐첵 소프트클릭스 채혈기+채혈침 25개 액티브 퍼포마 인스턴트 가이드 란셋 채혈바늘 주식회사 더에스지엠
|
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- text: 녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어
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- text: HL 지닥터 혈당시험지 100매 /당뇨측정 검사지 스트립 1_지닥터 혈당시험지 100매+알콜솜100매 헬스라e프
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- text: 비디 울트라파인 인슐린 주사기 1박스 100개 328821[31G 8mm 0.5ml]BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G
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6mm 0.5ml 1박스(324901) 더메디칼샵
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inference: true
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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: metric
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value: 0.9786747905559787
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name: Metric
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---
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+
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# SetFit with mini1013/master_domain
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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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The model has been trained using an efficient few-shot learning technique that involves:
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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:** 3 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>'프리스타일 리브레 무채혈 연속혈당측정기(24년1월)얼라이브패치1매 거래명세서 광명헬스케어'</li><li>'SD 코드프리 혈당측정기(측정기+채혈기+침10매+파우치)P 스토어알파'</li><li>'올메디쿠스 글루코닥터 탑 혈당계 AGM-4100+파우치+채혈기+채혈침 10개 엠에스메디칼'</li></ul> |
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| 2.0 | <ul><li>'에스디 SD 코드프리 측정지|검사지|시험지 100매(25년 2월) 더메디칼샵'</li><li>'바로잰 당뇨검사 혈당시험지 100매(50매x2팩) 사용기한 25년 3월 MinSellAmount 유니프라이스'</li><li>'옵티엄 프리스타일 케톤시험지1박스10매 검사지 혈중 (24년 8월) 메디트리'</li></ul> |
|
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| 0.0 | <ul><li>'비디 울트라파인 인슐린 주사기 1박스 100입 324901 [31G 6mm 0.5ml] BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G 8mm 3/10ml(0.5단위) 1박스(320440) 더메디칼샵'</li><li>'BD 비디 울트라파인 인슐린 주사기 시린지 31G 6mm 1ml 324903 100입 주식회사 더에스지엠'</li><li>'정림 멸균 일회용 주사기 3cc 23g 25mm 100개입 멸균주사기 10cc 18G 38mm(100ea/pck) (주)케이디상사'</li></ul> |
|
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|
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## Evaluation
|
71 |
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|
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### Metrics
|
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| Label | Metric |
|
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|:--------|:-------|
|
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| **all** | 0.9787 |
|
76 |
+
|
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+
## Uses
|
78 |
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|
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### Direct Use for Inference
|
80 |
+
|
81 |
+
First install the SetFit library:
|
82 |
+
|
83 |
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```bash
|
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pip install setfit
|
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```
|
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|
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Then you can load this model and run inference.
|
88 |
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|
89 |
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```python
|
90 |
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from setfit import SetFitModel
|
91 |
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|
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# Download from the 🤗 Hub
|
93 |
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model = SetFitModel.from_pretrained("mini1013/master_cate_lh7")
|
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# Run inference
|
95 |
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preds = model("녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어")
|
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```
|
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|
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<!--
|
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### Downstream Use
|
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+
|
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*List how someone could finetune this model on their own dataset.*
|
102 |
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-->
|
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|
104 |
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<!--
|
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### Out-of-Scope Use
|
106 |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
108 |
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-->
|
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|
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<!--
|
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## Bias, Risks and Limitations
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|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
114 |
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-->
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|
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<!--
|
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### Recommendations
|
118 |
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|
119 |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
120 |
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-->
|
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|
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## Training Details
|
123 |
+
|
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### Training Set Metrics
|
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| Training set | Min | Median | Max |
|
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|:-------------|:----|:-------|:----|
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| Word count | 4 | 9.62 | 21 |
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|
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| Label | Training Sample Count |
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|:------|:----------------------|
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| 0.0 | 50 |
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| 1.0 | 50 |
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| 2.0 | 50 |
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### Training Hyperparameters
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- batch_size: (512, 512)
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- num_epochs: (20, 20)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 40
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- body_learning_rate: (2e-05, 2e-05)
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- head_learning_rate: 2e-05
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
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- use_amp: False
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- warmup_proportion: 0.1
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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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| 0.0417 | 1 | 0.4565 | - |
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| 2.0833 | 50 | 0.1836 | - |
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| 4.1667 | 100 | 0.1645 | - |
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| 6.25 | 150 | 0.0004 | - |
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| 8.3333 | 200 | 0.0001 | - |
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| 10.4167 | 250 | 0.0001 | - |
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| 12.5 | 300 | 0.0 | - |
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| 14.5833 | 350 | 0.0 | - |
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| 16.6667 | 400 | 0.0 | - |
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| 18.75 | 450 | 0.0 | - |
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### Framework Versions
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- Python: 3.10.12
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- SetFit: 1.1.0.dev0
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- Sentence Transformers: 3.1.1
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- Transformers: 4.46.1
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- PyTorch: 2.4.0+cu121
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- Datasets: 2.20.0
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- Tokenizers: 0.20.0
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## Citation
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### BibTeX
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```bibtex
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@article{https://doi.org/10.48550/arxiv.2209.11055,
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doi = {10.48550/ARXIV.2209.11055},
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url = {https://arxiv.org/abs/2209.11055},
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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},
|
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title = {Efficient Few-Shot Learning Without Prompts},
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publisher = {arXiv},
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
|
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|
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<!--
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## Model Card Authors
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*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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## 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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-->
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config.json
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{
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"_name_or_path": "mini1013/master_item_lh",
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"architectures": [
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"RobertaModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"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,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"tokenizer_class": "BertTokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.46.1",
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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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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "3.1.1",
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"transformers": "4.46.1",
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"pytorch": "2.4.0+cu121"
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},
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"prompts": {},
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8 |
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"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"normalize_embeddings": false,
|
3 |
+
"labels": null
|
4 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9511cee3967ca45ede389c52618c089519d539734e1888eca5b185aebc4b620f
|
3 |
+
size 442494816
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:94ff48844d414680a62e128a1d2f343335347640fb879c85152148beeabd0afc
|
3 |
+
size 19295
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
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|
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|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"cls_token": {
|
10 |
+
"content": "[CLS]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"eos_token": {
|
17 |
+
"content": "[SEP]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"mask_token": {
|
24 |
+
"content": "[MASK]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"pad_token": {
|
31 |
+
"content": "[PAD]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"sep_token": {
|
38 |
+
"content": "[SEP]",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
},
|
44 |
+
"unk_token": {
|
45 |
+
"content": "[UNK]",
|
46 |
+
"lstrip": false,
|
47 |
+
"normalized": false,
|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false
|
50 |
+
}
|
51 |
+
}
|
tokenizer.json
ADDED
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|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[CLS]",
|
5 |
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"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
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"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "[PAD]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "[SEP]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"3": {
|
28 |
+
"content": "[UNK]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"4": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"bos_token": "[CLS]",
|
45 |
+
"clean_up_tokenization_spaces": false,
|
46 |
+
"cls_token": "[CLS]",
|
47 |
+
"do_basic_tokenize": true,
|
48 |
+
"do_lower_case": false,
|
49 |
+
"eos_token": "[SEP]",
|
50 |
+
"mask_token": "[MASK]",
|
51 |
+
"max_length": 512,
|
52 |
+
"model_max_length": 512,
|
53 |
+
"never_split": null,
|
54 |
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"pad_to_multiple_of": null,
|
55 |
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"pad_token": "[PAD]",
|
56 |
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"pad_token_type_id": 0,
|
57 |
+
"padding_side": "right",
|
58 |
+
"sep_token": "[SEP]",
|
59 |
+
"stride": 0,
|
60 |
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"strip_accents": null,
|
61 |
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"tokenize_chinese_chars": true,
|
62 |
+
"tokenizer_class": "BertTokenizer",
|
63 |
+
"truncation_side": "right",
|
64 |
+
"truncation_strategy": "longest_first",
|
65 |
+
"unk_token": "[UNK]"
|
66 |
+
}
|
vocab.txt
ADDED
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See raw diff
|
|