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NTIS
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sroberta-embedding

Text Classification
setfit
PyTorch
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +833 -0
  3. config.json +29 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +7 -0
  6. model_head.pkl +3 -0
  7. modules.json +14 -0
  8. pytorch_model.bin +3 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +9 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +24 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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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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+ }
README.md ADDED
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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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+
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+ # SetFit with jhgan/ko-sroberta-multitask
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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 [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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+
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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:** [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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+
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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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+ | 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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+
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+ ## 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** | 0.9952 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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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.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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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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+ <!--
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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.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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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.*
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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.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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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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+
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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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+
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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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+
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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 | - |
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+ | 0.3727 | 2950 | 0.0 | - |
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+ | 0.3790 | 3000 | 0.0 | - |
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+ | 0.3853 | 3050 | 0.0 | - |
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+ | 0.3916 | 3100 | 0.0 | - |
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+ | 0.3979 | 3150 | 0.0 | - |
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+ | 0.4042 | 3200 | 0.0 | - |
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+ | 0.4106 | 3250 | 0.0 | - |
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+ | 0.4169 | 3300 | 0.0 | - |
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+ | 0.4232 | 3350 | 0.0 | - |
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+ | 0.4295 | 3400 | 0.0 | - |
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+ | 0.4358 | 3450 | 0.0 | - |
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+ | 0.4421 | 3500 | 0.0 | - |
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+ | 0.4485 | 3550 | 0.0 | - |
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+ | 0.4548 | 3600 | 0.0 | - |
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+ | 0.4611 | 3650 | 0.0 | - |
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+ | 0.4674 | 3700 | 0.0 | - |
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+ | 0.4737 | 3750 | 0.0 | - |
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+ | 0.4800 | 3800 | 0.0 | - |
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+ | 0.4864 | 3850 | 0.0 | - |
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+ | 0.4927 | 3900 | 0.0 | - |
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+ | 0.4990 | 3950 | 0.0 | - |
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+ | 0.5053 | 4000 | 0.0 | - |
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+ | 0.5116 | 4050 | 0.0 | - |
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+ | 0.5179 | 4100 | 0.0 | - |
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+ | 0.5243 | 4150 | 0.0 | - |
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+ | 0.5306 | 4200 | 0.0 | - |
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+ | 0.5369 | 4250 | 0.0 | - |
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+ | 0.5432 | 4300 | 0.0 | - |
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+ | 0.5495 | 4350 | 0.0004 | - |
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+ | 0.5558 | 4400 | 0.0001 | - |
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+ | 0.5622 | 4450 | 0.0 | - |
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+ | 0.5685 | 4500 | 0.0096 | - |
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+ | 0.5748 | 4550 | 0.0 | - |
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+ | 0.5811 | 4600 | 0.0 | - |
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+ | 0.5874 | 4650 | 0.0 | - |
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+ | 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 | - |
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+ | 0.6443 | 5100 | 0.0 | - |
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+ | 0.6506 | 5150 | 0.0 | - |
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+ | 0.6569 | 5200 | 0.0 | - |
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+ | 0.6632 | 5250 | 0.0 | - |
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+ | 0.6695 | 5300 | 0.0 | - |
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+ | 0.6758 | 5350 | 0.0 | - |
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+ | 0.6822 | 5400 | 0.0 | - |
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+ | 0.6885 | 5450 | 0.0 | - |
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+ | 0.6948 | 5500 | 0.0 | - |
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+ | 0.7011 | 5550 | 0.0 | - |
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+ | 0.7074 | 5600 | 0.0 | - |
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+ | 0.7137 | 5650 | 0.0 | - |
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+ | 0.7201 | 5700 | 0.0 | - |
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+ | 0.7264 | 5750 | 0.0 | - |
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+ | 0.7327 | 5800 | 0.0 | - |
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+ | 0.7390 | 5850 | 0.0 | - |
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+ | 0.7453 | 5900 | 0.0 | - |
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+ | 0.7516 | 5950 | 0.0 | - |
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+ | 0.7580 | 6000 | 0.0 | - |
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+ | 0.7643 | 6050 | 0.0 | - |
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+ | 0.7706 | 6100 | 0.0 | - |
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+ | 0.7769 | 6150 | 0.0 | - |
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+ | 0.7832 | 6200 | 0.0 | - |
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+ | 0.7895 | 6250 | 0.0 | - |
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+ | 0.7959 | 6300 | 0.0 | - |
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+ | 0.8022 | 6350 | 0.0 | - |
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+ | 0.8085 | 6400 | 0.0 | - |
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+ | 0.8148 | 6450 | 0.0 | - |
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+ | 0.8211 | 6500 | 0.0 | - |
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+ | 0.8274 | 6550 | 0.0 | - |
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+ | 0.8338 | 6600 | 0.0 | - |
285
+ | 0.8401 | 6650 | 0.0 | - |
286
+ | 0.8464 | 6700 | 0.0 | - |
287
+ | 0.8527 | 6750 | 0.0 | - |
288
+ | 0.8590 | 6800 | 0.0 | - |
289
+ | 0.8653 | 6850 | 0.0 | - |
290
+ | 0.8717 | 6900 | 0.0 | - |
291
+ | 0.8780 | 6950 | 0.0 | - |
292
+ | 0.8843 | 7000 | 0.0 | - |
293
+ | 0.8906 | 7050 | 0.0 | - |
294
+ | 0.8969 | 7100 | 0.0 | - |
295
+ | 0.9032 | 7150 | 0.0 | - |
296
+ | 0.9096 | 7200 | 0.0 | - |
297
+ | 0.9159 | 7250 | 0.0 | - |
298
+ | 0.9222 | 7300 | 0.0 | - |
299
+ | 0.9285 | 7350 | 0.0 | - |
300
+ | 0.9348 | 7400 | 0.0 | - |
301
+ | 0.9411 | 7450 | 0.0 | - |
302
+ | 0.9474 | 7500 | 0.0 | - |
303
+ | 0.9538 | 7550 | 0.0 | - |
304
+ | 0.9601 | 7600 | 0.0 | - |
305
+ | 0.9664 | 7650 | 0.0 | - |
306
+ | 0.9727 | 7700 | 0.0 | - |
307
+ | 0.9790 | 7750 | 0.0 | - |
308
+ | 0.9853 | 7800 | 0.0 | - |
309
+ | 0.9917 | 7850 | 0.0 | - |
310
+ | 0.9980 | 7900 | 0.0 | - |
311
+ | 1.0 | 7916 | - | 0.0096 |
312
+ | 1.0043 | 7950 | 0.0 | - |
313
+ | 1.0106 | 8000 | 0.0 | - |
314
+ | 1.0169 | 8050 | 0.0 | - |
315
+ | 1.0232 | 8100 | 0.0 | - |
316
+ | 1.0296 | 8150 | 0.0 | - |
317
+ | 1.0359 | 8200 | 0.0 | - |
318
+ | 1.0422 | 8250 | 0.0 | - |
319
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320
+ | 1.0548 | 8350 | 0.0 | - |
321
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322
+ | 1.0675 | 8450 | 0.0 | - |
323
+ | 1.0738 | 8500 | 0.0 | - |
324
+ | 1.0801 | 8550 | 0.0 | - |
325
+ | 1.0864 | 8600 | 0.0 | - |
326
+ | 1.0927 | 8650 | 0.0 | - |
327
+ | 1.0990 | 8700 | 0.0 | - |
328
+ | 1.1054 | 8750 | 0.0 | - |
329
+ | 1.1117 | 8800 | 0.0 | - |
330
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331
+ | 1.1243 | 8900 | 0.0 | - |
332
+ | 1.1306 | 8950 | 0.0 | - |
333
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334
+ | 1.1433 | 9050 | 0.0 | - |
335
+ | 1.1496 | 9100 | 0.0 | - |
336
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337
+ | 1.1622 | 9200 | 0.0 | - |
338
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339
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340
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341
+ | 1.1875 | 9400 | 0.0 | - |
342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
+ | **2.0** | **15832** | **-** | **0.0096** |
471
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472
+ | 2.0086 | 15900 | 0.0 | - |
473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
+ | 2.1791 | 17250 | 0.0 | - |
500
+ | 2.1854 | 17300 | 0.0 | - |
501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
+ | 2.3749 | 18800 | 0.0 | - |
531
+ | 2.3813 | 18850 | 0.0 | - |
532
+ | 2.3876 | 18900 | 0.0 | - |
533
+ | 2.3939 | 18950 | 0.0 | - |
534
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535
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536
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537
+ | 2.4192 | 19150 | 0.0 | - |
538
+ | 2.4255 | 19200 | 0.0 | - |
539
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540
+ | 2.4381 | 19300 | 0.0 | - |
541
+ | 2.4444 | 19350 | 0.0 | - |
542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
+ | 2.5707 | 20350 | 0.0 | - |
562
+ | 2.5771 | 20400 | 0.0 | - |
563
+ | 2.5834 | 20450 | 0.0 | - |
564
+ | 2.5897 | 20500 | 0.0 | - |
565
+ | 2.5960 | 20550 | 0.0 | - |
566
+ | 2.6023 | 20600 | 0.0 | - |
567
+ | 2.6086 | 20650 | 0.0 | - |
568
+ | 2.6150 | 20700 | 0.0 | - |
569
+ | 2.6213 | 20750 | 0.0 | - |
570
+ | 2.6276 | 20800 | 0.0 | - |
571
+ | 2.6339 | 20850 | 0.0 | - |
572
+ | 2.6402 | 20900 | 0.0 | - |
573
+ | 2.6465 | 20950 | 0.0 | - |
574
+ | 2.6529 | 21000 | 0.0 | - |
575
+ | 2.6592 | 21050 | 0.0 | - |
576
+ | 2.6655 | 21100 | 0.0 | - |
577
+ | 2.6718 | 21150 | 0.0 | - |
578
+ | 2.6781 | 21200 | 0.0 | - |
579
+ | 2.6844 | 21250 | 0.0 | - |
580
+ | 2.6908 | 21300 | 0.0 | - |
581
+ | 2.6971 | 21350 | 0.0 | - |
582
+ | 2.7034 | 21400 | 0.0 | - |
583
+ | 2.7097 | 21450 | 0.0 | - |
584
+ | 2.7160 | 21500 | 0.0 | - |
585
+ | 2.7223 | 21550 | 0.0 | - |
586
+ | 2.7287 | 21600 | 0.0 | - |
587
+ | 2.7350 | 21650 | 0.0 | - |
588
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589
+ | 2.7476 | 21750 | 0.0 | - |
590
+ | 2.7539 | 21800 | 0.0 | - |
591
+ | 2.7602 | 21850 | 0.0 | - |
592
+ | 2.7665 | 21900 | 0.0 | - |
593
+ | 2.7729 | 21950 | 0.0 | - |
594
+ | 2.7792 | 22000 | 0.0 | - |
595
+ | 2.7855 | 22050 | 0.0 | - |
596
+ | 2.7918 | 22100 | 0.0 | - |
597
+ | 2.7981 | 22150 | 0.0 | - |
598
+ | 2.8044 | 22200 | 0.0 | - |
599
+ | 2.8108 | 22250 | 0.0 | - |
600
+ | 2.8171 | 22300 | 0.0 | - |
601
+ | 2.8234 | 22350 | 0.0 | - |
602
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603
+ | 2.8360 | 22450 | 0.0 | - |
604
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605
+ | 2.8487 | 22550 | 0.0 | - |
606
+ | 2.8550 | 22600 | 0.0 | - |
607
+ | 2.8613 | 22650 | 0.0 | - |
608
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609
+ | 2.8739 | 22750 | 0.0 | - |
610
+ | 2.8802 | 22800 | 0.0 | - |
611
+ | 2.8866 | 22850 | 0.0 | - |
612
+ | 2.8929 | 22900 | 0.0 | - |
613
+ | 2.8992 | 22950 | 0.0 | - |
614
+ | 2.9055 | 23000 | 0.0 | - |
615
+ | 2.9118 | 23050 | 0.0 | - |
616
+ | 2.9181 | 23100 | 0.0 | - |
617
+ | 2.9245 | 23150 | 0.0 | - |
618
+ | 2.9308 | 23200 | 0.0 | - |
619
+ | 2.9371 | 23250 | 0.0 | - |
620
+ | 2.9434 | 23300 | 0.0 | - |
621
+ | 2.9497 | 23350 | 0.0 | - |
622
+ | 2.9560 | 23400 | 0.0 | - |
623
+ | 2.9624 | 23450 | 0.0 | - |
624
+ | 2.9687 | 23500 | 0.0 | - |
625
+ | 2.9750 | 23550 | 0.0 | - |
626
+ | 2.9813 | 23600 | 0.0 | - |
627
+ | 2.9876 | 23650 | 0.0 | - |
628
+ | 2.9939 | 23700 | 0.0 | - |
629
+ | 3.0 | 23748 | - | 0.0128 |
630
+ | 3.0003 | 23750 | 0.0 | - |
631
+ | 3.0066 | 23800 | 0.0 | - |
632
+ | 3.0129 | 23850 | 0.0 | - |
633
+ | 3.0192 | 23900 | 0.0 | - |
634
+ | 3.0255 | 23950 | 0.0 | - |
635
+ | 3.0318 | 24000 | 0.0 | - |
636
+ | 3.0382 | 24050 | 0.0 | - |
637
+ | 3.0445 | 24100 | 0.0 | - |
638
+ | 3.0508 | 24150 | 0.0 | - |
639
+ | 3.0571 | 24200 | 0.0 | - |
640
+ | 3.0634 | 24250 | 0.0 | - |
641
+ | 3.0697 | 24300 | 0.0 | - |
642
+ | 3.0760 | 24350 | 0.0 | - |
643
+ | 3.0824 | 24400 | 0.0 | - |
644
+ | 3.0887 | 24450 | 0.0 | - |
645
+ | 3.0950 | 24500 | 0.0 | - |
646
+ | 3.1013 | 24550 | 0.0 | - |
647
+ | 3.1076 | 24600 | 0.0 | - |
648
+ | 3.1139 | 24650 | 0.0 | - |
649
+ | 3.1203 | 24700 | 0.0 | - |
650
+ | 3.1266 | 24750 | 0.0 | - |
651
+ | 3.1329 | 24800 | 0.0 | - |
652
+ | 3.1392 | 24850 | 0.0 | - |
653
+ | 3.1455 | 24900 | 0.0 | - |
654
+ | 3.1518 | 24950 | 0.0 | - |
655
+ | 3.1582 | 25000 | 0.0 | - |
656
+ | 3.1645 | 25050 | 0.0 | - |
657
+ | 3.1708 | 25100 | 0.0 | - |
658
+ | 3.1771 | 25150 | 0.0 | - |
659
+ | 3.1834 | 25200 | 0.0 | - |
660
+ | 3.1897 | 25250 | 0.0 | - |
661
+ | 3.1961 | 25300 | 0.0 | - |
662
+ | 3.2024 | 25350 | 0.0 | - |
663
+ | 3.2087 | 25400 | 0.0 | - |
664
+ | 3.2150 | 25450 | 0.0 | - |
665
+ | 3.2213 | 25500 | 0.0 | - |
666
+ | 3.2276 | 25550 | 0.0 | - |
667
+ | 3.2340 | 25600 | 0.0 | - |
668
+ | 3.2403 | 25650 | 0.0 | - |
669
+ | 3.2466 | 25700 | 0.0 | - |
670
+ | 3.2529 | 25750 | 0.0 | - |
671
+ | 3.2592 | 25800 | 0.0 | - |
672
+ | 3.2655 | 25850 | 0.0 | - |
673
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674
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+
791
+ * The bold row denotes the saved checkpoint.
792
+ ### Framework Versions
793
+ - Python: 3.9.18
794
+ - SetFit: 1.0.3
795
+ - Sentence Transformers: 2.2.1
796
+ - Transformers: 4.32.1
797
+ - PyTorch: 1.10.0
798
+ - Datasets: 2.20.0
799
+ - Tokenizers: 0.13.3
800
+
801
+ ## Citation
802
+
803
+ ### BibTeX
804
+ ```bibtex
805
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
806
+ doi = {10.48550/ARXIV.2209.11055},
807
+ url = {https://arxiv.org/abs/2209.11055},
808
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
809
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
810
+ title = {Efficient Few-Shot Learning Without Prompts},
811
+ publisher = {arXiv},
812
+ year = {2022},
813
+ copyright = {Creative Commons Attribution 4.0 International}
814
+ }
815
+ ```
816
+
817
+ <!--
818
+ ## Glossary
819
+
820
+ *Clearly define terms in order to be accessible across audiences.*
821
+ -->
822
+
823
+ <!--
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+ ## Model Card Authors
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+
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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.*
827
+ -->
828
+
829
+ <!--
830
+ ## Model Card Contact
831
+
832
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
833
+ -->
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