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--- |
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base_model: jhgan/ko-sroberta-multitask |
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library_name: setfit |
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metrics: |
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- accuracy |
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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: 제36조에 따른 수탁기관 정보 공시 방법은? |
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- text: 원장 인계 전 필요한 절차는? |
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- text: 미국에서 I140 허가 통지서 사본을 받으려면 어떻게 해야 하나요? |
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- text: 기술자문계획서 작성 시 연구일정과 기술보유자 선발 고려 이유는? |
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- text: 연구윤리활동비와 연구실안전관리비의 공통 경비 관리는? |
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inference: true |
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model-index: |
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- name: SetFit with jhgan/ko-sroberta-multitask |
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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: 0.9951690821256038 |
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name: Accuracy |
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--- |
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# SetFit with jhgan/ko-sroberta-multitask |
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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 [jhgan/ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask) 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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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [jhgan/ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask) |
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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:** 128 tokens |
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- **Number of Classes:** 2 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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### Model Sources |
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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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### Model Labels |
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| Label | Examples | |
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|:--------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| rag | <ul><li>'QR코드 스캔 후 필요한 서류와 절차는?'</li><li>'연구용역사업의 원가계산서 관련, 일정 금액 이상 지출 승인은 누구에게 받나요?'</li><li>'계약부서 승인 없이 지급신청 시 주의할 점은?'</li></ul> | |
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| general | <ul><li>'아래 글의 요지 좀 설명해줘.\n \n 다른 문화권에서 온 여자와 데이트. 관계에 대해 좋은 점이 많이 있습니다. 공통된 직업적 관심사, 동일한 성욕, 그리고 서로를 존중한다는 점은 제게는 새로운 관계입니다(항상 남성에 대해 안 좋은 태도를 가진 여자들과만 사귀어 왔죠). 그녀는 저를 정말 사랑해요. \n \n 하지만 장기적인 생존 가능성에 대해 몇 가지 심각한 우려가 있습니다. 하나는 부모님에 관한 것입니다. 제 부모님은 우리가 사귀는 사이라는 사실을 알게 되자 "네가 미국에 머물 수 있는 티켓이라는 걸 기억하라"고 말씀하셨어요. 우리가 진짜 사귀는 사이라는 사실을 알게 된 부모님은 제가 얼마나 버는지 알고 싶어 하셨고(저는 대학원생입니다), 존경의 표시로 은퇴한 부모님을 부양하는 전통에 대해 제가 괜찮은지 확인하고 싶어 하셨습니다(부모님은 그런 도움이 필요 없을 만큼 잘 살고 계시지만요). 여자친구는 이에 대해 부모님의 의견에 동의하며 제가 괜찮지 않다면 돈을 더 벌어서 직접 해야 한다고 말했습니다. 또한 여자친구는 제가 이전에 결혼했고 지금은 이혼했다는 사실을 부모님이 \'절대 알 수 없다\'고 말합니다. \n \n 제가 극복하거나 간과할 수 있었던 다른 문제들도 있지만(한 가지 예로, 그녀는 사교적이지 않지만 저는 사교적입니다), 이러한 문제들이 결합되어 그녀와의 미래는 앞으로 많은 문제가 예고되어 있고 위험하다고 느낍니다. 이전 결혼 생활에서 저는 그런 징후를 무시하고 대가를 치렀고, 그 역사를 반복하고 싶지 않습니다. 동시에 저와 성적으로도 잘 어울리는 파트너가 있다는 것은 정말 좋은 일입니다. \n \n 다른 사람들은 이런 다문화적인 상황에서 어떤 경험을 했는지, 특히 장기적인 경험이 있다면 어떤지 궁금합니다.'</li><li>'너는 누구냐니까'</li><li>'문제와 몇 가지 답 옵션("A", "B", "C", "D"와 연관된)이 주어집니다. 상식적인 지식을 바탕으로 정답을 선택해야 합니다. 연상에 기반한 답은 피하고, 답안 세트는 연상을 넘어서는 상식을 파악하기 위해 의도적으로 선택된 것입니다. \'A\', \'B\', \'C\', \'D\', \'E\' 중 하나를 제외하고는 다른 문자를 생성하지 말고 각 문제에 대해 하나의 답만 작성하세요.\n\n폰이라는 이름은 매우 다재다능할 수 있지만, 모든 부품이 중요한 것은 무엇일까요?\n(A)체스 게임 (B)계획 (C)체스 세트 (D)체커 (E)노스 캐롤라이나'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.9952 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("NTIS/sroberta-embedding") |
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# Run inference |
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preds = model("원장 인계 전 필요한 절차는?") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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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.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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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 | 2 | 24.824 | 722 | |
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| Label | Training Sample Count | |
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|:--------|:----------------------| |
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| rag | 553 | |
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| general | 447 | |
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### Training Hyperparameters |
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- batch_size: (64, 64) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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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 |
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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: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:---------:|:-------------:|:---------------:| |
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| 0.0001 | 1 | 0.2655 | - | |
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| 0.0063 | 50 | 0.2091 | - | |
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| 0.0126 | 100 | 0.2327 | - | |
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| 0.0189 | 150 | 0.1578 | - | |
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| 0.0253 | 200 | 0.0836 | - | |
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| 0.0316 | 250 | 0.0274 | - | |
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| 0.0379 | 300 | 0.0068 | - | |
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| 0.0442 | 350 | 0.0032 | - | |
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| 0.0505 | 400 | 0.0013 | - | |
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| 0.0568 | 450 | 0.0012 | - | |
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| 0.0632 | 500 | 0.0009 | - | |
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| 0.0695 | 550 | 0.0006 | - | |
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| 0.0758 | 600 | 0.0004 | - | |
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| 0.0821 | 650 | 0.0004 | - | |
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| 0.0884 | 700 | 0.0003 | - | |
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| 0.0947 | 750 | 0.0003 | - | |
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| 0.1011 | 800 | 0.0003 | - | |
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| 0.1074 | 850 | 0.0002 | - | |
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| 0.1137 | 900 | 0.0002 | - | |
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| 0.1200 | 950 | 0.0002 | - | |
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| 0.1263 | 1000 | 0.0002 | - | |
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| 0.1326 | 1050 | 0.0001 | - | |
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| 0.1390 | 1100 | 0.0001 | - | |
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| 0.1453 | 1150 | 0.0001 | - | |
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| 0.1516 | 1200 | 0.0001 | - | |
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| 0.1579 | 1250 | 0.0001 | - | |
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| 0.1642 | 1300 | 0.0001 | - | |
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| 0.1705 | 1350 | 0.0001 | - | |
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| 0.1769 | 1400 | 0.0001 | - | |
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| 0.1832 | 1450 | 0.0001 | - | |
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| 0.1895 | 1500 | 0.0001 | - | |
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| 0.1958 | 1550 | 0.0001 | - | |
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| 0.2021 | 1600 | 0.0 | - | |
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| 0.2084 | 1650 | 0.0001 | - | |
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| 0.2148 | 1700 | 0.0001 | - | |
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| 0.2211 | 1750 | 0.0 | - | |
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| 0.2274 | 1800 | 0.0001 | - | |
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| 0.2337 | 1850 | 0.0 | - | |
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| 0.2400 | 1900 | 0.0 | - | |
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| 0.2463 | 1950 | 0.0 | - | |
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| 0.2527 | 2000 | 0.0 | - | |
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| 0.2590 | 2050 | 0.0 | - | |
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| 0.2653 | 2100 | 0.0 | - | |
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| 0.2716 | 2150 | 0.0 | - | |
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| 0.2779 | 2200 | 0.0 | - | |
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| 0.2842 | 2250 | 0.0 | - | |
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| 0.2906 | 2300 | 0.0 | - | |
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| 0.2969 | 2350 | 0.0 | - | |
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| 0.3032 | 2400 | 0.0 | - | |
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| 0.3095 | 2450 | 0.0 | - | |
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| 0.3158 | 2500 | 0.0 | - | |
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| 0.3221 | 2550 | 0.0 | - | |
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| 0.3284 | 2600 | 0.0 | - | |
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| 0.3348 | 2650 | 0.0 | - | |
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| 0.3411 | 2700 | 0.0 | - | |
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| 0.3474 | 2750 | 0.0 | - | |
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| 0.3537 | 2800 | 0.0 | - | |
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| 0.3600 | 2850 | 0.0 | - | |
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| 0.3663 | 2900 | 0.0 | - | |
|
| 0.3727 | 2950 | 0.0 | - | |
|
| 0.3790 | 3000 | 0.0 | - | |
|
| 0.3853 | 3050 | 0.0 | - | |
|
| 0.3916 | 3100 | 0.0 | - | |
|
| 0.3979 | 3150 | 0.0 | - | |
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| 0.4042 | 3200 | 0.0 | - | |
|
| 0.4106 | 3250 | 0.0 | - | |
|
| 0.4169 | 3300 | 0.0 | - | |
|
| 0.4232 | 3350 | 0.0 | - | |
|
| 0.4295 | 3400 | 0.0 | - | |
|
| 0.4358 | 3450 | 0.0 | - | |
|
| 0.4421 | 3500 | 0.0 | - | |
|
| 0.4485 | 3550 | 0.0 | - | |
|
| 0.4548 | 3600 | 0.0 | - | |
|
| 0.4611 | 3650 | 0.0 | - | |
|
| 0.4674 | 3700 | 0.0 | - | |
|
| 0.4737 | 3750 | 0.0 | - | |
|
| 0.4800 | 3800 | 0.0 | - | |
|
| 0.4864 | 3850 | 0.0 | - | |
|
| 0.4927 | 3900 | 0.0 | - | |
|
| 0.4990 | 3950 | 0.0 | - | |
|
| 0.5053 | 4000 | 0.0 | - | |
|
| 0.5116 | 4050 | 0.0 | - | |
|
| 0.5179 | 4100 | 0.0 | - | |
|
| 0.5243 | 4150 | 0.0 | - | |
|
| 0.5306 | 4200 | 0.0 | - | |
|
| 0.5369 | 4250 | 0.0 | - | |
|
| 0.5432 | 4300 | 0.0 | - | |
|
| 0.5495 | 4350 | 0.0004 | - | |
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| 0.5558 | 4400 | 0.0001 | - | |
|
| 0.5622 | 4450 | 0.0 | - | |
|
| 0.5685 | 4500 | 0.0096 | - | |
|
| 0.5748 | 4550 | 0.0 | - | |
|
| 0.5811 | 4600 | 0.0 | - | |
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| 0.5874 | 4650 | 0.0 | - | |
|
| 0.5937 | 4700 | 0.0 | - | |
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| 0.6001 | 4750 | 0.0 | - | |
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| 0.6064 | 4800 | 0.0 | - | |
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| 0.6127 | 4850 | 0.0 | - | |
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| 0.6190 | 4900 | 0.0 | - | |
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| 0.6253 | 4950 | 0.0 | - | |
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| 0.6316 | 5000 | 0.0 | - | |
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| 0.6379 | 5050 | 0.0 | - | |
|
| 0.6443 | 5100 | 0.0 | - | |
|
| 0.6506 | 5150 | 0.0 | - | |
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| 0.6569 | 5200 | 0.0 | - | |
|
| 0.6632 | 5250 | 0.0 | - | |
|
| 0.6695 | 5300 | 0.0 | - | |
|
| 0.6758 | 5350 | 0.0 | - | |
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| 0.6822 | 5400 | 0.0 | - | |
|
| 0.6885 | 5450 | 0.0 | - | |
|
| 0.6948 | 5500 | 0.0 | - | |
|
| 0.7011 | 5550 | 0.0 | - | |
|
| 0.7074 | 5600 | 0.0 | - | |
|
| 0.7137 | 5650 | 0.0 | - | |
|
| 0.7201 | 5700 | 0.0 | - | |
|
| 0.7264 | 5750 | 0.0 | - | |
|
| 0.7327 | 5800 | 0.0 | - | |
|
| 0.7390 | 5850 | 0.0 | - | |
|
| 0.7453 | 5900 | 0.0 | - | |
|
| 0.7516 | 5950 | 0.0 | - | |
|
| 0.7580 | 6000 | 0.0 | - | |
|
| 0.7643 | 6050 | 0.0 | - | |
|
| 0.7706 | 6100 | 0.0 | - | |
|
| 0.7769 | 6150 | 0.0 | - | |
|
| 0.7832 | 6200 | 0.0 | - | |
|
| 0.7895 | 6250 | 0.0 | - | |
|
| 0.7959 | 6300 | 0.0 | - | |
|
| 0.8022 | 6350 | 0.0 | - | |
|
| 0.8085 | 6400 | 0.0 | - | |
|
| 0.8148 | 6450 | 0.0 | - | |
|
| 0.8211 | 6500 | 0.0 | - | |
|
| 0.8274 | 6550 | 0.0 | - | |
|
| 0.8338 | 6600 | 0.0 | - | |
|
| 0.8401 | 6650 | 0.0 | - | |
|
| 0.8464 | 6700 | 0.0 | - | |
|
| 0.8527 | 6750 | 0.0 | - | |
|
| 0.8590 | 6800 | 0.0 | - | |
|
| 0.8653 | 6850 | 0.0 | - | |
|
| 0.8717 | 6900 | 0.0 | - | |
|
| 0.8780 | 6950 | 0.0 | - | |
|
| 0.8843 | 7000 | 0.0 | - | |
|
| 0.8906 | 7050 | 0.0 | - | |
|
| 0.8969 | 7100 | 0.0 | - | |
|
| 0.9032 | 7150 | 0.0 | - | |
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| 0.9096 | 7200 | 0.0 | - | |
|
| 0.9159 | 7250 | 0.0 | - | |
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| 0.9222 | 7300 | 0.0 | - | |
|
| 0.9285 | 7350 | 0.0 | - | |
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| 0.9348 | 7400 | 0.0 | - | |
|
| 0.9411 | 7450 | 0.0 | - | |
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| 0.9474 | 7500 | 0.0 | - | |
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| 0.9538 | 7550 | 0.0 | - | |
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| 0.9601 | 7600 | 0.0 | - | |
|
| 0.9664 | 7650 | 0.0 | - | |
|
| 0.9727 | 7700 | 0.0 | - | |
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| 0.9790 | 7750 | 0.0 | - | |
|
| 0.9853 | 7800 | 0.0 | - | |
|
| 0.9917 | 7850 | 0.0 | - | |
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| 0.9980 | 7900 | 0.0 | - | |
|
| 1.0 | 7916 | - | 0.0096 | |
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| 1.0043 | 7950 | 0.0 | - | |
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| 1.0106 | 8000 | 0.0 | - | |
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| 1.0169 | 8050 | 0.0 | - | |
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| 1.0232 | 8100 | 0.0 | - | |
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| 1.0296 | 8150 | 0.0 | - | |
|
| 1.0359 | 8200 | 0.0 | - | |
|
| 1.0422 | 8250 | 0.0 | - | |
|
| 1.0485 | 8300 | 0.0 | - | |
|
| 1.0548 | 8350 | 0.0 | - | |
|
| 1.0611 | 8400 | 0.0 | - | |
|
| 1.0675 | 8450 | 0.0 | - | |
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| 1.0738 | 8500 | 0.0 | - | |
|
| 1.0801 | 8550 | 0.0 | - | |
|
| 1.0864 | 8600 | 0.0 | - | |
|
| 1.0927 | 8650 | 0.0 | - | |
|
| 1.0990 | 8700 | 0.0 | - | |
|
| 1.1054 | 8750 | 0.0 | - | |
|
| 1.1117 | 8800 | 0.0 | - | |
|
| 1.1180 | 8850 | 0.0 | - | |
|
| 1.1243 | 8900 | 0.0 | - | |
|
| 1.1306 | 8950 | 0.0 | - | |
|
| 1.1369 | 9000 | 0.0 | - | |
|
| 1.1433 | 9050 | 0.0 | - | |
|
| 1.1496 | 9100 | 0.0 | - | |
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| 1.1559 | 9150 | 0.0 | - | |
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| 1.1622 | 9200 | 0.0 | - | |
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| 1.1685 | 9250 | 0.0 | - | |
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| 1.1748 | 9300 | 0.0 | - | |
|
| 1.1812 | 9350 | 0.0 | - | |
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| 1.1875 | 9400 | 0.0 | - | |
|
| 1.1938 | 9450 | 0.0 | - | |
|
| 1.2001 | 9500 | 0.0 | - | |
|
| 1.2064 | 9550 | 0.0 | - | |
|
| 1.2127 | 9600 | 0.0 | - | |
|
| 1.2191 | 9650 | 0.0 | - | |
|
| 1.2254 | 9700 | 0.0 | - | |
|
| 1.2317 | 9750 | 0.0 | - | |
|
| 1.2380 | 9800 | 0.0 | - | |
|
| 1.2443 | 9850 | 0.0 | - | |
|
| 1.2506 | 9900 | 0.0 | - | |
|
| 1.2569 | 9950 | 0.0 | - | |
|
| 1.2633 | 10000 | 0.0 | - | |
|
| 1.2696 | 10050 | 0.0 | - | |
|
| 1.2759 | 10100 | 0.0 | - | |
|
| 1.2822 | 10150 | 0.0 | - | |
|
| 1.2885 | 10200 | 0.0 | - | |
|
| 1.2948 | 10250 | 0.0 | - | |
|
| 1.3012 | 10300 | 0.0 | - | |
|
| 1.3075 | 10350 | 0.0 | - | |
|
| 1.3138 | 10400 | 0.0 | - | |
|
| 1.3201 | 10450 | 0.0 | - | |
|
| 1.3264 | 10500 | 0.0 | - | |
|
| 1.3327 | 10550 | 0.0 | - | |
|
| 1.3391 | 10600 | 0.0 | - | |
|
| 1.3454 | 10650 | 0.0 | - | |
|
| 1.3517 | 10700 | 0.0 | - | |
|
| 1.3580 | 10750 | 0.0 | - | |
|
| 1.3643 | 10800 | 0.0 | - | |
|
| 1.3706 | 10850 | 0.0 | - | |
|
| 1.3770 | 10900 | 0.0 | - | |
|
| 1.3833 | 10950 | 0.0 | - | |
|
| 1.3896 | 11000 | 0.0 | - | |
|
| 1.3959 | 11050 | 0.0 | - | |
|
| 1.4022 | 11100 | 0.0 | - | |
|
| 1.4085 | 11150 | 0.0 | - | |
|
| 1.4149 | 11200 | 0.0 | - | |
|
| 1.4212 | 11250 | 0.0 | - | |
|
| 1.4275 | 11300 | 0.0 | - | |
|
| 1.4338 | 11350 | 0.0 | - | |
|
| 1.4401 | 11400 | 0.0 | - | |
|
| 1.4464 | 11450 | 0.0 | - | |
|
| 1.4528 | 11500 | 0.0 | - | |
|
| 1.4591 | 11550 | 0.0 | - | |
|
| 1.4654 | 11600 | 0.0 | - | |
|
| 1.4717 | 11650 | 0.0 | - | |
|
| 1.4780 | 11700 | 0.0 | - | |
|
| 1.4843 | 11750 | 0.0 | - | |
|
| 1.4907 | 11800 | 0.0 | - | |
|
| 1.4970 | 11850 | 0.0 | - | |
|
| 1.5033 | 11900 | 0.0 | - | |
|
| 1.5096 | 11950 | 0.0 | - | |
|
| 1.5159 | 12000 | 0.0 | - | |
|
| 1.5222 | 12050 | 0.0 | - | |
|
| 1.5285 | 12100 | 0.0 | - | |
|
| 1.5349 | 12150 | 0.0 | - | |
|
| 1.5412 | 12200 | 0.0 | - | |
|
| 1.5475 | 12250 | 0.0 | - | |
|
| 1.5538 | 12300 | 0.0 | - | |
|
| 1.5601 | 12350 | 0.0 | - | |
|
| 1.5664 | 12400 | 0.0 | - | |
|
| 1.5728 | 12450 | 0.0 | - | |
|
| 1.5791 | 12500 | 0.0 | - | |
|
| 1.5854 | 12550 | 0.0 | - | |
|
| 1.5917 | 12600 | 0.0 | - | |
|
| 1.5980 | 12650 | 0.0 | - | |
|
| 1.6043 | 12700 | 0.0 | - | |
|
| 1.6107 | 12750 | 0.0 | - | |
|
| 1.6170 | 12800 | 0.0 | - | |
|
| 1.6233 | 12850 | 0.0 | - | |
|
| 1.6296 | 12900 | 0.0 | - | |
|
| 1.6359 | 12950 | 0.0 | - | |
|
| 1.6422 | 13000 | 0.0 | - | |
|
| 1.6486 | 13050 | 0.0 | - | |
|
| 1.6549 | 13100 | 0.0 | - | |
|
| 1.6612 | 13150 | 0.0 | - | |
|
| 1.6675 | 13200 | 0.0 | - | |
|
| 1.6738 | 13250 | 0.0 | - | |
|
| 1.6801 | 13300 | 0.0 | - | |
|
| 1.6865 | 13350 | 0.0 | - | |
|
| 1.6928 | 13400 | 0.0 | - | |
|
| 1.6991 | 13450 | 0.0 | - | |
|
| 1.7054 | 13500 | 0.0 | - | |
|
| 1.7117 | 13550 | 0.0 | - | |
|
| 1.7180 | 13600 | 0.0 | - | |
|
| 1.7244 | 13650 | 0.0 | - | |
|
| 1.7307 | 13700 | 0.0 | - | |
|
| 1.7370 | 13750 | 0.0 | - | |
|
| 1.7433 | 13800 | 0.0 | - | |
|
| 1.7496 | 13850 | 0.0 | - | |
|
| 1.7559 | 13900 | 0.0 | - | |
|
| 1.7623 | 13950 | 0.0 | - | |
|
| 1.7686 | 14000 | 0.0 | - | |
|
| 1.7749 | 14050 | 0.0 | - | |
|
| 1.7812 | 14100 | 0.0 | - | |
|
| 1.7875 | 14150 | 0.0 | - | |
|
| 1.7938 | 14200 | 0.0 | - | |
|
| 1.8002 | 14250 | 0.0 | - | |
|
| 1.8065 | 14300 | 0.0 | - | |
|
| 1.8128 | 14350 | 0.0 | - | |
|
| 1.8191 | 14400 | 0.0 | - | |
|
| 1.8254 | 14450 | 0.0 | - | |
|
| 1.8317 | 14500 | 0.0 | - | |
|
| 1.8380 | 14550 | 0.0 | - | |
|
| 1.8444 | 14600 | 0.0 | - | |
|
| 1.8507 | 14650 | 0.0 | - | |
|
| 1.8570 | 14700 | 0.0 | - | |
|
| 1.8633 | 14750 | 0.0 | - | |
|
| 1.8696 | 14800 | 0.0 | - | |
|
| 1.8759 | 14850 | 0.0 | - | |
|
| 1.8823 | 14900 | 0.0 | - | |
|
| 1.8886 | 14950 | 0.0 | - | |
|
| 1.8949 | 15000 | 0.0 | - | |
|
| 1.9012 | 15050 | 0.0 | - | |
|
| 1.9075 | 15100 | 0.0 | - | |
|
| 1.9138 | 15150 | 0.0 | - | |
|
| 1.9202 | 15200 | 0.0 | - | |
|
| 1.9265 | 15250 | 0.0 | - | |
|
| 1.9328 | 15300 | 0.0 | - | |
|
| 1.9391 | 15350 | 0.0 | - | |
|
| 1.9454 | 15400 | 0.0 | - | |
|
| 1.9517 | 15450 | 0.0 | - | |
|
| 1.9581 | 15500 | 0.0 | - | |
|
| 1.9644 | 15550 | 0.0 | - | |
|
| 1.9707 | 15600 | 0.0 | - | |
|
| 1.9770 | 15650 | 0.0 | - | |
|
| 1.9833 | 15700 | 0.0 | - | |
|
| 1.9896 | 15750 | 0.0 | - | |
|
| 1.9960 | 15800 | 0.0 | - | |
|
| **2.0** | **15832** | **-** | **0.0096** | |
|
| 2.0023 | 15850 | 0.0 | - | |
|
| 2.0086 | 15900 | 0.0 | - | |
|
| 2.0149 | 15950 | 0.0 | - | |
|
| 2.0212 | 16000 | 0.0 | - | |
|
| 2.0275 | 16050 | 0.0 | - | |
|
| 2.0339 | 16100 | 0.0 | - | |
|
| 2.0402 | 16150 | 0.0 | - | |
|
| 2.0465 | 16200 | 0.0 | - | |
|
| 2.0528 | 16250 | 0.0 | - | |
|
| 2.0591 | 16300 | 0.0 | - | |
|
| 2.0654 | 16350 | 0.0 | - | |
|
| 2.0718 | 16400 | 0.0 | - | |
|
| 2.0781 | 16450 | 0.0 | - | |
|
| 2.0844 | 16500 | 0.0 | - | |
|
| 2.0907 | 16550 | 0.0 | - | |
|
| 2.0970 | 16600 | 0.0 | - | |
|
| 2.1033 | 16650 | 0.0 | - | |
|
| 2.1097 | 16700 | 0.0 | - | |
|
| 2.1160 | 16750 | 0.0 | - | |
|
| 2.1223 | 16800 | 0.0 | - | |
|
| 2.1286 | 16850 | 0.0 | - | |
|
| 2.1349 | 16900 | 0.0 | - | |
|
| 2.1412 | 16950 | 0.0 | - | |
|
| 2.1475 | 17000 | 0.0 | - | |
|
| 2.1539 | 17050 | 0.0 | - | |
|
| 2.1602 | 17100 | 0.0 | - | |
|
| 2.1665 | 17150 | 0.0 | - | |
|
| 2.1728 | 17200 | 0.0 | - | |
|
| 2.1791 | 17250 | 0.0 | - | |
|
| 2.1854 | 17300 | 0.0 | - | |
|
| 2.1918 | 17350 | 0.0 | - | |
|
| 2.1981 | 17400 | 0.0 | - | |
|
| 2.2044 | 17450 | 0.0 | - | |
|
| 2.2107 | 17500 | 0.0 | - | |
|
| 2.2170 | 17550 | 0.0 | - | |
|
| 2.2233 | 17600 | 0.0 | - | |
|
| 2.2297 | 17650 | 0.0 | - | |
|
| 2.2360 | 17700 | 0.0 | - | |
|
| 2.2423 | 17750 | 0.0 | - | |
|
| 2.2486 | 17800 | 0.0 | - | |
|
| 2.2549 | 17850 | 0.0 | - | |
|
| 2.2612 | 17900 | 0.0 | - | |
|
| 2.2676 | 17950 | 0.0 | - | |
|
| 2.2739 | 18000 | 0.0 | - | |
|
| 2.2802 | 18050 | 0.0 | - | |
|
| 2.2865 | 18100 | 0.0 | - | |
|
| 2.2928 | 18150 | 0.0 | - | |
|
| 2.2991 | 18200 | 0.0 | - | |
|
| 2.3055 | 18250 | 0.0 | - | |
|
| 2.3118 | 18300 | 0.0 | - | |
|
| 2.3181 | 18350 | 0.0 | - | |
|
| 2.3244 | 18400 | 0.0 | - | |
|
| 2.3307 | 18450 | 0.0 | - | |
|
| 2.3370 | 18500 | 0.0 | - | |
|
| 2.3434 | 18550 | 0.0 | - | |
|
| 2.3497 | 18600 | 0.0 | - | |
|
| 2.3560 | 18650 | 0.0 | - | |
|
| 2.3623 | 18700 | 0.0 | - | |
|
| 2.3686 | 18750 | 0.0 | - | |
|
| 2.3749 | 18800 | 0.0 | - | |
|
| 2.3813 | 18850 | 0.0 | - | |
|
| 2.3876 | 18900 | 0.0 | - | |
|
| 2.3939 | 18950 | 0.0 | - | |
|
| 2.4002 | 19000 | 0.0 | - | |
|
| 2.4065 | 19050 | 0.0 | - | |
|
| 2.4128 | 19100 | 0.0 | - | |
|
| 2.4192 | 19150 | 0.0 | - | |
|
| 2.4255 | 19200 | 0.0 | - | |
|
| 2.4318 | 19250 | 0.0 | - | |
|
| 2.4381 | 19300 | 0.0 | - | |
|
| 2.4444 | 19350 | 0.0 | - | |
|
| 2.4507 | 19400 | 0.0 | - | |
|
| 2.4570 | 19450 | 0.0 | - | |
|
| 2.4634 | 19500 | 0.0 | - | |
|
| 2.4697 | 19550 | 0.0 | - | |
|
| 2.4760 | 19600 | 0.0 | - | |
|
| 2.4823 | 19650 | 0.0 | - | |
|
| 2.4886 | 19700 | 0.0 | - | |
|
| 2.4949 | 19750 | 0.0 | - | |
|
| 2.5013 | 19800 | 0.0 | - | |
|
| 2.5076 | 19850 | 0.0 | - | |
|
| 2.5139 | 19900 | 0.0 | - | |
|
| 2.5202 | 19950 | 0.0 | - | |
|
| 2.5265 | 20000 | 0.0 | - | |
|
| 2.5328 | 20050 | 0.0 | - | |
|
| 2.5392 | 20100 | 0.0 | - | |
|
| 2.5455 | 20150 | 0.0 | - | |
|
| 2.5518 | 20200 | 0.0 | - | |
|
| 2.5581 | 20250 | 0.0 | - | |
|
| 2.5644 | 20300 | 0.0 | - | |
|
| 2.5707 | 20350 | 0.0 | - | |
|
| 2.5771 | 20400 | 0.0 | - | |
|
| 2.5834 | 20450 | 0.0 | - | |
|
| 2.5897 | 20500 | 0.0 | - | |
|
| 2.5960 | 20550 | 0.0 | - | |
|
| 2.6023 | 20600 | 0.0 | - | |
|
| 2.6086 | 20650 | 0.0 | - | |
|
| 2.6150 | 20700 | 0.0 | - | |
|
| 2.6213 | 20750 | 0.0 | - | |
|
| 2.6276 | 20800 | 0.0 | - | |
|
| 2.6339 | 20850 | 0.0 | - | |
|
| 2.6402 | 20900 | 0.0 | - | |
|
| 2.6465 | 20950 | 0.0 | - | |
|
| 2.6529 | 21000 | 0.0 | - | |
|
| 2.6592 | 21050 | 0.0 | - | |
|
| 2.6655 | 21100 | 0.0 | - | |
|
| 2.6718 | 21150 | 0.0 | - | |
|
| 2.6781 | 21200 | 0.0 | - | |
|
| 2.6844 | 21250 | 0.0 | - | |
|
| 2.6908 | 21300 | 0.0 | - | |
|
| 2.6971 | 21350 | 0.0 | - | |
|
| 2.7034 | 21400 | 0.0 | - | |
|
| 2.7097 | 21450 | 0.0 | - | |
|
| 2.7160 | 21500 | 0.0 | - | |
|
| 2.7223 | 21550 | 0.0 | - | |
|
| 2.7287 | 21600 | 0.0 | - | |
|
| 2.7350 | 21650 | 0.0 | - | |
|
| 2.7413 | 21700 | 0.0 | - | |
|
| 2.7476 | 21750 | 0.0 | - | |
|
| 2.7539 | 21800 | 0.0 | - | |
|
| 2.7602 | 21850 | 0.0 | - | |
|
| 2.7665 | 21900 | 0.0 | - | |
|
| 2.7729 | 21950 | 0.0 | - | |
|
| 2.7792 | 22000 | 0.0 | - | |
|
| 2.7855 | 22050 | 0.0 | - | |
|
| 2.7918 | 22100 | 0.0 | - | |
|
| 2.7981 | 22150 | 0.0 | - | |
|
| 2.8044 | 22200 | 0.0 | - | |
|
| 2.8108 | 22250 | 0.0 | - | |
|
| 2.8171 | 22300 | 0.0 | - | |
|
| 2.8234 | 22350 | 0.0 | - | |
|
| 2.8297 | 22400 | 0.0 | - | |
|
| 2.8360 | 22450 | 0.0 | - | |
|
| 2.8423 | 22500 | 0.0 | - | |
|
| 2.8487 | 22550 | 0.0 | - | |
|
| 2.8550 | 22600 | 0.0 | - | |
|
| 2.8613 | 22650 | 0.0 | - | |
|
| 2.8676 | 22700 | 0.0 | - | |
|
| 2.8739 | 22750 | 0.0 | - | |
|
| 2.8802 | 22800 | 0.0 | - | |
|
| 2.8866 | 22850 | 0.0 | - | |
|
| 2.8929 | 22900 | 0.0 | - | |
|
| 2.8992 | 22950 | 0.0 | - | |
|
| 2.9055 | 23000 | 0.0 | - | |
|
| 2.9118 | 23050 | 0.0 | - | |
|
| 2.9181 | 23100 | 0.0 | - | |
|
| 2.9245 | 23150 | 0.0 | - | |
|
| 2.9308 | 23200 | 0.0 | - | |
|
| 2.9371 | 23250 | 0.0 | - | |
|
| 2.9434 | 23300 | 0.0 | - | |
|
| 2.9497 | 23350 | 0.0 | - | |
|
| 2.9560 | 23400 | 0.0 | - | |
|
| 2.9624 | 23450 | 0.0 | - | |
|
| 2.9687 | 23500 | 0.0 | - | |
|
| 2.9750 | 23550 | 0.0 | - | |
|
| 2.9813 | 23600 | 0.0 | - | |
|
| 2.9876 | 23650 | 0.0 | - | |
|
| 2.9939 | 23700 | 0.0 | - | |
|
| 3.0 | 23748 | - | 0.0128 | |
|
| 3.0003 | 23750 | 0.0 | - | |
|
| 3.0066 | 23800 | 0.0 | - | |
|
| 3.0129 | 23850 | 0.0 | - | |
|
| 3.0192 | 23900 | 0.0 | - | |
|
| 3.0255 | 23950 | 0.0 | - | |
|
| 3.0318 | 24000 | 0.0 | - | |
|
| 3.0382 | 24050 | 0.0 | - | |
|
| 3.0445 | 24100 | 0.0 | - | |
|
| 3.0508 | 24150 | 0.0 | - | |
|
| 3.0571 | 24200 | 0.0 | - | |
|
| 3.0634 | 24250 | 0.0 | - | |
|
| 3.0697 | 24300 | 0.0 | - | |
|
| 3.0760 | 24350 | 0.0 | - | |
|
| 3.0824 | 24400 | 0.0 | - | |
|
| 3.0887 | 24450 | 0.0 | - | |
|
| 3.0950 | 24500 | 0.0 | - | |
|
| 3.1013 | 24550 | 0.0 | - | |
|
| 3.1076 | 24600 | 0.0 | - | |
|
| 3.1139 | 24650 | 0.0 | - | |
|
| 3.1203 | 24700 | 0.0 | - | |
|
| 3.1266 | 24750 | 0.0 | - | |
|
| 3.1329 | 24800 | 0.0 | - | |
|
| 3.1392 | 24850 | 0.0 | - | |
|
| 3.1455 | 24900 | 0.0 | - | |
|
| 3.1518 | 24950 | 0.0 | - | |
|
| 3.1582 | 25000 | 0.0 | - | |
|
| 3.1645 | 25050 | 0.0 | - | |
|
| 3.1708 | 25100 | 0.0 | - | |
|
| 3.1771 | 25150 | 0.0 | - | |
|
| 3.1834 | 25200 | 0.0 | - | |
|
| 3.1897 | 25250 | 0.0 | - | |
|
| 3.1961 | 25300 | 0.0 | - | |
|
| 3.2024 | 25350 | 0.0 | - | |
|
| 3.2087 | 25400 | 0.0 | - | |
|
| 3.2150 | 25450 | 0.0 | - | |
|
| 3.2213 | 25500 | 0.0 | - | |
|
| 3.2276 | 25550 | 0.0 | - | |
|
| 3.2340 | 25600 | 0.0 | - | |
|
| 3.2403 | 25650 | 0.0 | - | |
|
| 3.2466 | 25700 | 0.0 | - | |
|
| 3.2529 | 25750 | 0.0 | - | |
|
| 3.2592 | 25800 | 0.0 | - | |
|
| 3.2655 | 25850 | 0.0 | - | |
|
| 3.2719 | 25900 | 0.0 | - | |
|
| 3.2782 | 25950 | 0.0 | - | |
|
| 3.2845 | 26000 | 0.0 | - | |
|
| 3.2908 | 26050 | 0.0 | - | |
|
| 3.2971 | 26100 | 0.0 | - | |
|
| 3.3034 | 26150 | 0.0 | - | |
|
| 3.3098 | 26200 | 0.0 | - | |
|
| 3.3161 | 26250 | 0.0 | - | |
|
| 3.3224 | 26300 | 0.0 | - | |
|
| 3.3287 | 26350 | 0.0 | - | |
|
| 3.3350 | 26400 | 0.0 | - | |
|
| 3.3413 | 26450 | 0.0 | - | |
|
| 3.3477 | 26500 | 0.0 | - | |
|
| 3.3540 | 26550 | 0.0 | - | |
|
| 3.3603 | 26600 | 0.0 | - | |
|
| 3.3666 | 26650 | 0.0 | - | |
|
| 3.3729 | 26700 | 0.0 | - | |
|
| 3.3792 | 26750 | 0.0 | - | |
|
| 3.3855 | 26800 | 0.0 | - | |
|
| 3.3919 | 26850 | 0.0 | - | |
|
| 3.3982 | 26900 | 0.0 | - | |
|
| 3.4045 | 26950 | 0.0 | - | |
|
| 3.4108 | 27000 | 0.0 | - | |
|
| 3.4171 | 27050 | 0.0 | - | |
|
| 3.4234 | 27100 | 0.0 | - | |
|
| 3.4298 | 27150 | 0.0 | - | |
|
| 3.4361 | 27200 | 0.0 | - | |
|
| 3.4424 | 27250 | 0.0 | - | |
|
| 3.4487 | 27300 | 0.0 | - | |
|
| 3.4550 | 27350 | 0.0 | - | |
|
| 3.4613 | 27400 | 0.0 | - | |
|
| 3.4677 | 27450 | 0.0 | - | |
|
| 3.4740 | 27500 | 0.0 | - | |
|
| 3.4803 | 27550 | 0.0 | - | |
|
| 3.4866 | 27600 | 0.0 | - | |
|
| 3.4929 | 27650 | 0.0 | - | |
|
| 3.4992 | 27700 | 0.0 | - | |
|
| 3.5056 | 27750 | 0.0 | - | |
|
| 3.5119 | 27800 | 0.0 | - | |
|
| 3.5182 | 27850 | 0.0 | - | |
|
| 3.5245 | 27900 | 0.0 | - | |
|
| 3.5308 | 27950 | 0.0 | - | |
|
| 3.5371 | 28000 | 0.0 | - | |
|
| 3.5435 | 28050 | 0.0 | - | |
|
| 3.5498 | 28100 | 0.0 | - | |
|
| 3.5561 | 28150 | 0.0 | - | |
|
| 3.5624 | 28200 | 0.0 | - | |
|
| 3.5687 | 28250 | 0.0 | - | |
|
| 3.5750 | 28300 | 0.0 | - | |
|
| 3.5814 | 28350 | 0.0 | - | |
|
| 3.5877 | 28400 | 0.0 | - | |
|
| 3.5940 | 28450 | 0.0 | - | |
|
| 3.6003 | 28500 | 0.0 | - | |
|
| 3.6066 | 28550 | 0.0 | - | |
|
| 3.6129 | 28600 | 0.0 | - | |
|
| 3.6193 | 28650 | 0.0 | - | |
|
| 3.6256 | 28700 | 0.0 | - | |
|
| 3.6319 | 28750 | 0.0 | - | |
|
| 3.6382 | 28800 | 0.0 | - | |
|
| 3.6445 | 28850 | 0.0 | - | |
|
| 3.6508 | 28900 | 0.0 | - | |
|
| 3.6572 | 28950 | 0.0 | - | |
|
| 3.6635 | 29000 | 0.0 | - | |
|
| 3.6698 | 29050 | 0.0 | - | |
|
| 3.6761 | 29100 | 0.0 | - | |
|
| 3.6824 | 29150 | 0.0 | - | |
|
| 3.6887 | 29200 | 0.0 | - | |
|
| 3.6950 | 29250 | 0.0 | - | |
|
| 3.7014 | 29300 | 0.0 | - | |
|
| 3.7077 | 29350 | 0.0 | - | |
|
| 3.7140 | 29400 | 0.0 | - | |
|
| 3.7203 | 29450 | 0.0 | - | |
|
| 3.7266 | 29500 | 0.0 | - | |
|
| 3.7329 | 29550 | 0.0 | - | |
|
| 3.7393 | 29600 | 0.0 | - | |
|
| 3.7456 | 29650 | 0.0 | - | |
|
| 3.7519 | 29700 | 0.0 | - | |
|
| 3.7582 | 29750 | 0.0 | - | |
|
| 3.7645 | 29800 | 0.0 | - | |
|
| 3.7708 | 29850 | 0.0 | - | |
|
| 3.7772 | 29900 | 0.0 | - | |
|
| 3.7835 | 29950 | 0.0 | - | |
|
| 3.7898 | 30000 | 0.0 | - | |
|
| 3.7961 | 30050 | 0.0 | - | |
|
| 3.8024 | 30100 | 0.0 | - | |
|
| 3.8087 | 30150 | 0.0 | - | |
|
| 3.8151 | 30200 | 0.0 | - | |
|
| 3.8214 | 30250 | 0.0 | - | |
|
| 3.8277 | 30300 | 0.0 | - | |
|
| 3.8340 | 30350 | 0.0 | - | |
|
| 3.8403 | 30400 | 0.0 | - | |
|
| 3.8466 | 30450 | 0.0 | - | |
|
| 3.8530 | 30500 | 0.0 | - | |
|
| 3.8593 | 30550 | 0.0 | - | |
|
| 3.8656 | 30600 | 0.0 | - | |
|
| 3.8719 | 30650 | 0.0 | - | |
|
| 3.8782 | 30700 | 0.0 | - | |
|
| 3.8845 | 30750 | 0.0 | - | |
|
| 3.8909 | 30800 | 0.0 | - | |
|
| 3.8972 | 30850 | 0.0 | - | |
|
| 3.9035 | 30900 | 0.0 | - | |
|
| 3.9098 | 30950 | 0.0 | - | |
|
| 3.9161 | 31000 | 0.0 | - | |
|
| 3.9224 | 31050 | 0.0 | - | |
|
| 3.9288 | 31100 | 0.0 | - | |
|
| 3.9351 | 31150 | 0.0 | - | |
|
| 3.9414 | 31200 | 0.0 | - | |
|
| 3.9477 | 31250 | 0.0 | - | |
|
| 3.9540 | 31300 | 0.0 | - | |
|
| 3.9603 | 31350 | 0.0 | - | |
|
| 3.9666 | 31400 | 0.0 | - | |
|
| 3.9730 | 31450 | 0.0 | - | |
|
| 3.9793 | 31500 | 0.0 | - | |
|
| 3.9856 | 31550 | 0.0 | - | |
|
| 3.9919 | 31600 | 0.0 | - | |
|
| 3.9982 | 31650 | 0.0 | - | |
|
| 4.0 | 31664 | - | 0.0117 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
### Framework Versions |
|
- Python: 3.9.18 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 2.2.1 |
|
- Transformers: 4.32.1 |
|
- PyTorch: 1.10.0 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.13.3 |
|
|
|
## 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} |
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} |
|
``` |
|
|
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