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---
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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: μΈκ³΅μ§λ₯ μ±λ΄ κΈ°μ ν₯μμ λν μμ΄λμ΄κ° μλλ°, κ΄λ ¨λ μμ μμΈν μ λ³΄κ° λ΄κΈ΄ λ
Όλ¬Έμ΄λ λ³΄κ³ μλ₯Ό μ°Ύμμ€λμ?
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- text: μ°κ΅¬ μλ£μ μλ‘ λΆλΆμ ν μ€λ‘ μμ½ν΄ μ€ μ μλμ?
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- text: μ°λ¦¬ νμ¬μ HR μ μ±
κ°μ λ°©μμ λν κ³Όμ λ₯Ό μ§ν μ€μ΄μΌ. κ°μ μ£Όμ μ μ΄μ κ³Όμ μ μ΄λ€ λΆλΆμμ μ μ€λ³΅λμλμ§ κΆκΈν΄
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- text: μ΄μ λ체μ μκ³ μ¨λμ κ΄ν μ°κ΅¬ μλ£λ₯Ό λͺ¨μΌκ³ μμ΄μ. μ¬κΈ°μ κ΄λ ¨λ μ μ¬ν μ°κ΅¬λ λ³΄κ³ μλ₯Ό μΆμ²λ°κ³ μΆμ΄μ
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- text: 곡μ₯μμ λ°μνλ κ°μ€ λμΆ λ¬Έμ ν΄κ²°μ μν μμ€ν
μ κ°λ°νλ €κ³ νλλ°, μ΄μ κ°μ μΈ‘λ©΄μμ μ§νλ κΈ°μ‘΄ μ°κ΅¬λ λΉμ·ν νλ‘μ νΈκ°
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μλμ§ μλ €μ£ΌμΈμ
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inference: true
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model-index:
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- name: SetFit
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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: 0.9891304347826086
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name: Accuracy
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---
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# SetFit
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
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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:** 5 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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| μ€νμ νμ§ | <ul><li>'κ±΄μΆ νλ‘μ νΈ μ€λͺ
λ¬Έμ₯μμ μ€νλ μλͺ»λ λ§μΆ€λ²μ μ°Ύμμ€.'</li><li>'κ²½μ λ³΄κ³ μ λ΄μ©μ λν μ€νμλ₯Ό κ²ν νκ³ μμ ν΄ λ릴 μ μμκΉμ?'</li><li>'κ²½μμ¬ λΆμ νλͺ© λ΄ λ¬Έμ₯ ꡬμ±μ μ€λ₯λ₯Ό μ§μ ν΄μ£Όκ² μ΅λκΉ?'</li></ul> |
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| μμ½ | <ul><li>'(νΉμ λ
Όλ¬Έ μ λͺ©)μ κ²°λ‘ λ° ν₯ν μ°κ΅¬ λ°©ν₯μ λν΄ μμ μ μ λ¦¬ν΄ μ£ΌμΈμ.'</li><li>'(νΉμ νΉνλ²νΈ)λ₯Ό κΈ°λ°μΌλ‘ ν λ°λͺ
μ μ 체μ μΈ κ°λ
μ μ§§κ² μ€λͺ
λΆνλ립λλ€.'</li><li>'1μ₯μ λ°μ΄ν° μμ§ κΈ°μ μ λν΄ μμ½ν΄μ£ΌμΈμ'</li></ul> |
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| μ μ¬λ¬Έμ | <ul><li>'5G ν΅μ λͺ¨λ μ΅μ νμ κ΄λ ¨λ νλ‘μ νΈλ₯Ό νκ³ μλλ°, λΉμ·ν λ΄μ©μ νλ‘μ νΈλ λ
Όλ¬Έμ΄ μλμ§ μ°κ²°ν΄μ λ§ν΄μ€λ?'</li><li>'AI κΈ°λ° ν¬μ€μΌμ΄ μ루μ
κ°λ°μ κ΄ν λ¬Έν μ‘°μ¬λ₯Ό νκ³ μμ΅λλ€. μ κ°μ μ£Όμ λ₯Ό λ€λ£¬ λ¬Έμλ₯Ό μ°Ύμμ€ μ μμκΉμ?'</li><li>'AI μ°μ° μλλ₯Ό μ΅μ ννκΈ° μν λ°λ체 μ€κ³ λ°©μμ μ°κ΅¬νκ³ μμ΄. κ΄λ ¨λ μ μ¬ν λ
Όλ¬Έμ΄λ λ³΄κ³ μλ₯Ό μ°Ύκ³ μΆμ΄'</li></ul> |
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| μ€λ³΅μ± κ²ν | <ul><li>'5G ν΅μ λ§μ κΈ°λ°μΌλ‘ μ€λ§νΈ μν° κ΅¬μΆμ κ΄ν μ°κ΅¬λ₯Ό μμνμ΄. μ΄μ λμΌνκ±°λ κ²ΉμΉλ μ°κ΅¬ κ³Όμ λ νλ‘μ νΈκ° μλμ§ μμλ΄μ£Όκ³ , μ΄μ λ λͺ
ννκ² λ°νμ€'</li><li>'건물μ λ΄μ§ μ€κ³ κ°ν λ°©μμ μ‘°μ¬νκ³ μλλ° μ΄μ μ°κ΄λ κΈ°μ‘΄ νλ‘μ νΈκ° 무μμ΄ μλμ§ κ·Έλ¦¬κ³ μ κ²ΉμΉλμ§ λ§ν΄μ€λ?'</li><li>'κ³ μ±λ₯ λ©λͺ¨λ¦¬ μμμ λ΄κ΅¬μ±μ ν₯μμν€λ κΈ°μ μ κ°λ°νκ³ μμ΄. μ΄μ λΉμ·ν κ³Όμ κ° μ΄μ μ μμλμ§, κ·Έλ¦¬κ³ μ΄λ»κ² μ μ¬νκ±°λ μ€λ³΅λλμ§ λ§ν΄μ€'</li></ul> |
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| νΉν μ§μμ 보 μ 곡 | <ul><li>'3D κΈμ λ°°μ κΈ°μ (HBM, TSV)μ λμ
μΌλ‘ μΈν μ λ ₯ μλΉ κ°μ λ°©μμλ μ΄λ€ κ²μ΄ μλκ°μ?'</li><li>'AI μν¬λ‘λλ₯Ό μ²λ¦¬νκΈ° μν λ°λ체 μν€ν
μ² μ€κ³μμλ μ΄λ€ μ λ΅λ€μ΄ μ¬μ©λλμ?'</li><li>'LEED μΈμ¦μ κΈ°μ€κ³Ό νλ κ³Όμ μ λν΄ μκ³ μΆμ΅λλ€.'</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** | 0.9891 |
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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 π€ Hub
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model = SetFitModel.from_pretrained("NTIS/kepri-embedding")
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# Run inference
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preds = model("μ°κ΅¬ μλ£μ μλ‘ λΆλΆμ ν μ€λ‘ μμ½ν΄ μ€ μ μλμ?")
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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 | 6 | 12.4709 | 27 |
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| Label | Training Sample Count |
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|:-----------|:----------------------|
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| μμ½ | 105 |
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| μ€λ³΅μ± κ²ν | 78 |
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| νΉν μ§μμ 보 μ 곡 | 106 |
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| μ μ¬λ¬Έμ | 115 |
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| μ€νμ νμ§ | 95 |
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### Training Hyperparameters
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- batch_size: (64, 64)
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- num_epochs: (10, 10)
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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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| 0.0003 | 1 | 0.2062 | - |
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| 0.0161 | 50 | 0.2314 | - |
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| 0.0322 | 100 | 0.2008 | - |
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| 0.0484 | 150 | 0.1395 | - |
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| 0.0645 | 200 | 0.11 | - |
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| 0.0806 | 250 | 0.0872 | - |
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| 0.0967 | 300 | 0.0462 | - |
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| 0.1129 | 350 | 0.0188 | - |
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| 0.1290 | 400 | 0.0201 | - |
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| 0.1451 | 450 | 0.025 | - |
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| 0.1612 | 500 | 0.004 | - |
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| 0.1774 | 550 | 0.002 | - |
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| 0.1935 | 600 | 0.0153 | - |
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| 0.2096 | 650 | 0.0011 | - |
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| 0.2257 | 700 | 0.0007 | - |
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| 0.2419 | 750 | 0.0006 | - |
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| 0.2580 | 800 | 0.0006 | - |
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| 0.2741 | 850 | 0.0005 | - |
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| 0.2902 | 900 | 0.0004 | - |
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| 0.3064 | 950 | 0.0005 | - |
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| 0.3225 | 1000 | 0.0002 | - |
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| 0.3386 | 1050 | 0.0002 | - |
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| 0.3547 | 1100 | 0.0003 | - |
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| 0.3708 | 1150 | 0.0002 | - |
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| 0.3870 | 1200 | 0.0002 | - |
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| 0.4031 | 1250 | 0.0002 | - |
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| 0.4192 | 1300 | 0.0001 | - |
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| 0.4353 | 1350 | 0.0002 | - |
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| 0.4515 | 1400 | 0.0001 | - |
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| 0.4676 | 1450 | 0.0001 | - |
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| 0.4837 | 1500 | 0.0001 | - |
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| 0.4998 | 1550 | 0.0001 | - |
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| 0.5160 | 1600 | 0.0001 | - |
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| 0.5321 | 1650 | 0.0001 | - |
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| 0.5482 | 1700 | 0.0001 | - |
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| 0.5643 | 1750 | 0.0001 | - |
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| 0.5805 | 1800 | 0.0001 | - |
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| 0.5966 | 1850 | 0.0001 | - |
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| 0.6127 | 1900 | 0.0001 | - |
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| 0.6288 | 1950 | 0.0001 | - |
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| 0.6450 | 2000 | 0.0001 | - |
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| 0.6611 | 2050 | 0.0001 | - |
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| 0.6772 | 2100 | 0.0001 | - |
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| 0.6933 | 2150 | 0.0001 | - |
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| 0.7094 | 2200 | 0.0001 | - |
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| 0.7256 | 2250 | 0.0001 | - |
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| 0.7417 | 2300 | 0.0001 | - |
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| 0.7578 | 2350 | 0.0001 | - |
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| 0.7739 | 2400 | 0.0001 | - |
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| 0.7901 | 2450 | 0.0001 | - |
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| 0.8062 | 2500 | 0.0001 | - |
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| 0.8223 | 2550 | 0.0001 | - |
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| 0.8384 | 2600 | 0.0 | - |
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| 0.8546 | 2650 | 0.0 | - |
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| 0.8707 | 2700 | 0.0 | - |
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| 0.8868 | 2750 | 0.0001 | - |
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| 0.9029 | 2800 | 0.0 | - |
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| 0.9191 | 2850 | 0.0001 | - |
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| 0.9352 | 2900 | 0.0 | - |
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| 0.9513 | 2950 | 0.0 | - |
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| 0.9674 | 3000 | 0.0 | - |
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| 0.9836 | 3050 | 0.0 | - |
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| 0.9997 | 3100 | 0.0 | - |
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| **1.0** | **3101** | **-** | **0.0247** |
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| 1.0158 | 3150 | 0.0 | - |
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| 1.0319 | 3200 | 0.0 | - |
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| 1.0480 | 3250 | 0.0 | - |
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| 1.0642 | 3300 | 0.0001 | - |
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| 1.0803 | 3350 | 0.0 | - |
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| 1.0964 | 3400 | 0.0 | - |
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| 1.1125 | 3450 | 0.0 | - |
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| 1.1287 | 3500 | 0.0 | - |
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| 1.1448 | 3550 | 0.0 | - |
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| 1.1609 | 3600 | 0.0 | - |
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| 1.1770 | 3650 | 0.0 | - |
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| 1.1932 | 3700 | 0.0 | - |
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| 1.2093 | 3750 | 0.0 | - |
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| 1.2254 | 3800 | 0.0 | - |
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| 1.2415 | 3850 | 0.0 | - |
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| 1.2577 | 3900 | 0.0 | - |
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| 1.2738 | 3950 | 0.0 | - |
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| 1.2899 | 4000 | 0.0 | - |
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| 1.3060 | 4050 | 0.0 | - |
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| 1.3222 | 4100 | 0.0 | - |
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| 1.3383 | 4150 | 0.0 | - |
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| 1.3544 | 4200 | 0.0 | - |
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| 1.3705 | 4250 | 0.0 | - |
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| 1.3866 | 4300 | 0.0 | - |
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| 1.4028 | 4350 | 0.0 | - |
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| 1.4189 | 4400 | 0.0 | - |
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| 1.4350 | 4450 | 0.0 | - |
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| 1.4511 | 4500 | 0.0 | - |
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| 1.4673 | 4550 | 0.0 | - |
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| 1.4834 | 4600 | 0.0 | - |
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| 1.4995 | 4650 | 0.0 | - |
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| 1.5156 | 4700 | 0.0 | - |
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| 1.5318 | 4750 | 0.0 | - |
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| 1.5479 | 4800 | 0.0 | - |
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| 1.5640 | 4850 | 0.0 | - |
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| 1.5801 | 4900 | 0.0 | - |
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| 1.5963 | 4950 | 0.0 | - |
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| 1.6124 | 5000 | 0.0 | - |
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| 1.6285 | 5050 | 0.0 | - |
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| 1.6446 | 5100 | 0.0 | - |
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| 1.6608 | 5150 | 0.0 | - |
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| 1.6769 | 5200 | 0.0 | - |
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| 1.6930 | 5250 | 0.0 | - |
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| 1.7091 | 5300 | 0.0 | - |
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| 1.7252 | 5350 | 0.0 | - |
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| 1.7414 | 5400 | 0.0 | - |
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| 1.7575 | 5450 | 0.0 | - |
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| 1.7736 | 5500 | 0.0 | - |
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| 1.7897 | 5550 | 0.0 | - |
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| 1.8059 | 5600 | 0.0 | - |
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| 1.8220 | 5650 | 0.0 | - |
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| 1.8381 | 5700 | 0.0 | - |
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| 1.8542 | 5750 | 0.0 | - |
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| 1.8704 | 5800 | 0.0 | - |
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| 1.8865 | 5850 | 0.0 | - |
|
| 277 |
-
| 1.9026 | 5900 | 0.0 | - |
|
| 278 |
-
| 1.9187 | 5950 | 0.0 | - |
|
| 279 |
-
| 1.9349 | 6000 | 0.0 | - |
|
| 280 |
-
| 1.9510 | 6050 | 0.0 | - |
|
| 281 |
-
| 1.9671 | 6100 | 0.0 | - |
|
| 282 |
-
| 1.9832 | 6150 | 0.0 | - |
|
| 283 |
-
| 1.9994 | 6200 | 0.0 | - |
|
| 284 |
-
| 2.0 | 6202 | - | 0.0262 |
|
| 285 |
-
| 2.0155 | 6250 | 0.0 | - |
|
| 286 |
-
| 2.0316 | 6300 | 0.0 | - |
|
| 287 |
-
| 2.0477 | 6350 | 0.0 | - |
|
| 288 |
-
| 2.0639 | 6400 | 0.0 | - |
|
| 289 |
-
| 2.0800 | 6450 | 0.0 | - |
|
| 290 |
-
| 2.0961 | 6500 | 0.0 | - |
|
| 291 |
-
| 2.1122 | 6550 | 0.0 | - |
|
| 292 |
-
| 2.1283 | 6600 | 0.0 | - |
|
| 293 |
-
| 2.1445 | 6650 | 0.0 | - |
|
| 294 |
-
| 2.1606 | 6700 | 0.0 | - |
|
| 295 |
-
| 2.1767 | 6750 | 0.0 | - |
|
| 296 |
-
| 2.1928 | 6800 | 0.0 | - |
|
| 297 |
-
| 2.2090 | 6850 | 0.0 | - |
|
| 298 |
-
| 2.2251 | 6900 | 0.0 | - |
|
| 299 |
-
| 2.2412 | 6950 | 0.0 | - |
|
| 300 |
-
| 2.2573 | 7000 | 0.0 | - |
|
| 301 |
-
| 2.2735 | 7050 | 0.0 | - |
|
| 302 |
-
| 2.2896 | 7100 | 0.0 | - |
|
| 303 |
-
| 2.3057 | 7150 | 0.0 | - |
|
| 304 |
-
| 2.3218 | 7200 | 0.0 | - |
|
| 305 |
-
| 2.3380 | 7250 | 0.0 | - |
|
| 306 |
-
| 2.3541 | 7300 | 0.0 | - |
|
| 307 |
-
| 2.3702 | 7350 | 0.0 | - |
|
| 308 |
-
| 2.3863 | 7400 | 0.0 | - |
|
| 309 |
-
| 2.4025 | 7450 | 0.0 | - |
|
| 310 |
-
| 2.4186 | 7500 | 0.0 | - |
|
| 311 |
-
| 2.4347 | 7550 | 0.0 | - |
|
| 312 |
-
| 2.4508 | 7600 | 0.0 | - |
|
| 313 |
-
| 2.4669 | 7650 | 0.0 | - |
|
| 314 |
-
| 2.4831 | 7700 | 0.0 | - |
|
| 315 |
-
| 2.4992 | 7750 | 0.0 | - |
|
| 316 |
-
| 2.5153 | 7800 | 0.0 | - |
|
| 317 |
-
| 2.5314 | 7850 | 0.0 | - |
|
| 318 |
-
| 2.5476 | 7900 | 0.0 | - |
|
| 319 |
-
| 2.5637 | 7950 | 0.0 | - |
|
| 320 |
-
| 2.5798 | 8000 | 0.0 | - |
|
| 321 |
-
| 2.5959 | 8050 | 0.0 | - |
|
| 322 |
-
| 2.6121 | 8100 | 0.0 | - |
|
| 323 |
-
| 2.6282 | 8150 | 0.0 | - |
|
| 324 |
-
| 2.6443 | 8200 | 0.0 | - |
|
| 325 |
-
| 2.6604 | 8250 | 0.0 | - |
|
| 326 |
-
| 2.6766 | 8300 | 0.0 | - |
|
| 327 |
-
| 2.6927 | 8350 | 0.0 | - |
|
| 328 |
-
| 2.7088 | 8400 | 0.0 | - |
|
| 329 |
-
| 2.7249 | 8450 | 0.0 | - |
|
| 330 |
-
| 2.7411 | 8500 | 0.0 | - |
|
| 331 |
-
| 2.7572 | 8550 | 0.0 | - |
|
| 332 |
-
| 2.7733 | 8600 | 0.0 | - |
|
| 333 |
-
| 2.7894 | 8650 | 0.0 | - |
|
| 334 |
-
| 2.8055 | 8700 | 0.0 | - |
|
| 335 |
-
| 2.8217 | 8750 | 0.0 | - |
|
| 336 |
-
| 2.8378 | 8800 | 0.0 | - |
|
| 337 |
-
| 2.8539 | 8850 | 0.0 | - |
|
| 338 |
-
| 2.8700 | 8900 | 0.0 | - |
|
| 339 |
-
| 2.8862 | 8950 | 0.0 | - |
|
| 340 |
-
| 2.9023 | 9000 | 0.0 | - |
|
| 341 |
-
| 2.9184 | 9050 | 0.0 | - |
|
| 342 |
-
| 2.9345 | 9100 | 0.0 | - |
|
| 343 |
-
| 2.9507 | 9150 | 0.0 | - |
|
| 344 |
-
| 2.9668 | 9200 | 0.0 | - |
|
| 345 |
-
| 2.9829 | 9250 | 0.0 | - |
|
| 346 |
-
| 2.9990 | 9300 | 0.0 | - |
|
| 347 |
-
| 3.0 | 9303 | - | 0.025 |
|
| 348 |
-
| 3.0152 | 9350 | 0.0 | - |
|
| 349 |
-
| 3.0313 | 9400 | 0.0 | - |
|
| 350 |
-
| 3.0474 | 9450 | 0.0 | - |
|
| 351 |
-
| 3.0635 | 9500 | 0.0 | - |
|
| 352 |
-
| 3.0797 | 9550 | 0.0 | - |
|
| 353 |
-
| 3.0958 | 9600 | 0.0 | - |
|
| 354 |
-
| 3.1119 | 9650 | 0.0 | - |
|
| 355 |
-
| 3.1280 | 9700 | 0.0 | - |
|
| 356 |
-
| 3.1441 | 9750 | 0.0 | - |
|
| 357 |
-
| 3.1603 | 9800 | 0.0 | - |
|
| 358 |
-
| 3.1764 | 9850 | 0.0 | - |
|
| 359 |
-
| 3.1925 | 9900 | 0.0 | - |
|
| 360 |
-
| 3.2086 | 9950 | 0.0 | - |
|
| 361 |
-
| 3.2248 | 10000 | 0.0 | - |
|
| 362 |
-
| 3.2409 | 10050 | 0.0 | - |
|
| 363 |
-
| 3.2570 | 10100 | 0.0 | - |
|
| 364 |
-
| 3.2731 | 10150 | 0.0 | - |
|
| 365 |
-
| 3.2893 | 10200 | 0.0 | - |
|
| 366 |
-
| 3.3054 | 10250 | 0.0 | - |
|
| 367 |
-
| 3.3215 | 10300 | 0.0 | - |
|
| 368 |
-
| 3.3376 | 10350 | 0.0 | - |
|
| 369 |
-
| 3.3538 | 10400 | 0.0 | - |
|
| 370 |
-
| 3.3699 | 10450 | 0.0 | - |
|
| 371 |
-
| 3.3860 | 10500 | 0.0 | - |
|
| 372 |
-
| 3.4021 | 10550 | 0.0 | - |
|
| 373 |
-
| 3.4183 | 10600 | 0.0 | - |
|
| 374 |
-
| 3.4344 | 10650 | 0.0 | - |
|
| 375 |
-
| 3.4505 | 10700 | 0.0 | - |
|
| 376 |
-
| 3.4666 | 10750 | 0.0083 | - |
|
| 377 |
-
| 3.4827 | 10800 | 0.0019 | - |
|
| 378 |
-
| 3.4989 | 10850 | 0.0001 | - |
|
| 379 |
-
| 3.5150 | 10900 | 0.0 | - |
|
| 380 |
-
| 3.5311 | 10950 | 0.001 | - |
|
| 381 |
-
| 3.5472 | 11000 | 0.0 | - |
|
| 382 |
-
| 3.5634 | 11050 | 0.0 | - |
|
| 383 |
-
| 3.5795 | 11100 | 0.0 | - |
|
| 384 |
-
| 3.5956 | 11150 | 0.0 | - |
|
| 385 |
-
| 3.6117 | 11200 | 0.0 | - |
|
| 386 |
-
| 3.6279 | 11250 | 0.0 | - |
|
| 387 |
-
| 3.6440 | 11300 | 0.0 | - |
|
| 388 |
-
| 3.6601 | 11350 | 0.0 | - |
|
| 389 |
-
| 3.6762 | 11400 | 0.0 | - |
|
| 390 |
-
| 3.6924 | 11450 | 0.0 | - |
|
| 391 |
-
| 3.7085 | 11500 | 0.0 | - |
|
| 392 |
-
| 3.7246 | 11550 | 0.0 | - |
|
| 393 |
-
| 3.7407 | 11600 | 0.0 | - |
|
| 394 |
-
| 3.7569 | 11650 | 0.0 | - |
|
| 395 |
-
| 3.7730 | 11700 | 0.0 | - |
|
| 396 |
-
| 3.7891 | 11750 | 0.0 | - |
|
| 397 |
-
| 3.8052 | 11800 | 0.0 | - |
|
| 398 |
-
| 3.8213 | 11850 | 0.0 | - |
|
| 399 |
-
| 3.8375 | 11900 | 0.0 | - |
|
| 400 |
-
| 3.8536 | 11950 | 0.0 | - |
|
| 401 |
-
| 3.8697 | 12000 | 0.0 | - |
|
| 402 |
-
| 3.8858 | 12050 | 0.0 | - |
|
| 403 |
-
| 3.9020 | 12100 | 0.0 | - |
|
| 404 |
-
| 3.9181 | 12150 | 0.0 | - |
|
| 405 |
-
| 3.9342 | 12200 | 0.0 | - |
|
| 406 |
-
| 3.9503 | 12250 | 0.0 | - |
|
| 407 |
-
| 3.9665 | 12300 | 0.0 | - |
|
| 408 |
-
| 3.9826 | 12350 | 0.0 | - |
|
| 409 |
-
| 3.9987 | 12400 | 0.0 | - |
|
| 410 |
-
| 4.0 | 12404 | - | 0.0253 |
|
| 411 |
-
| 4.0148 | 12450 | 0.0 | - |
|
| 412 |
-
| 4.0310 | 12500 | 0.0 | - |
|
| 413 |
-
| 4.0471 | 12550 | 0.0 | - |
|
| 414 |
-
| 4.0632 | 12600 | 0.0 | - |
|
| 415 |
-
| 4.0793 | 12650 | 0.0 | - |
|
| 416 |
-
| 4.0955 | 12700 | 0.0 | - |
|
| 417 |
-
| 4.1116 | 12750 | 0.0 | - |
|
| 418 |
-
| 4.1277 | 12800 | 0.0 | - |
|
| 419 |
-
| 4.1438 | 12850 | 0.0 | - |
|
| 420 |
-
| 4.1599 | 12900 | 0.0 | - |
|
| 421 |
-
| 4.1761 | 12950 | 0.0 | - |
|
| 422 |
-
| 4.1922 | 13000 | 0.0 | - |
|
| 423 |
-
| 4.2083 | 13050 | 0.0 | - |
|
| 424 |
-
| 4.2244 | 13100 | 0.0 | - |
|
| 425 |
-
| 4.2406 | 13150 | 0.0 | - |
|
| 426 |
-
| 4.2567 | 13200 | 0.0 | - |
|
| 427 |
-
| 4.2728 | 13250 | 0.0 | - |
|
| 428 |
-
| 4.2889 | 13300 | 0.0 | - |
|
| 429 |
-
| 4.3051 | 13350 | 0.0 | - |
|
| 430 |
-
| 4.3212 | 13400 | 0.0 | - |
|
| 431 |
-
| 4.3373 | 13450 | 0.0 | - |
|
| 432 |
-
| 4.3534 | 13500 | 0.0 | - |
|
| 433 |
-
| 4.3696 | 13550 | 0.0 | - |
|
| 434 |
-
| 4.3857 | 13600 | 0.0 | - |
|
| 435 |
-
| 4.4018 | 13650 | 0.0 | - |
|
| 436 |
-
| 4.4179 | 13700 | 0.0 | - |
|
| 437 |
-
| 4.4341 | 13750 | 0.0 | - |
|
| 438 |
-
| 4.4502 | 13800 | 0.0 | - |
|
| 439 |
-
| 4.4663 | 13850 | 0.0 | - |
|
| 440 |
-
| 4.4824 | 13900 | 0.0 | - |
|
| 441 |
-
| 4.4985 | 13950 | 0.0 | - |
|
| 442 |
-
| 4.5147 | 14000 | 0.0 | - |
|
| 443 |
-
| 4.5308 | 14050 | 0.0 | - |
|
| 444 |
-
| 4.5469 | 14100 | 0.0 | - |
|
| 445 |
-
| 4.5630 | 14150 | 0.0 | - |
|
| 446 |
-
| 4.5792 | 14200 | 0.0 | - |
|
| 447 |
-
| 4.5953 | 14250 | 0.0 | - |
|
| 448 |
-
| 4.6114 | 14300 | 0.0 | - |
|
| 449 |
-
| 4.6275 | 14350 | 0.0 | - |
|
| 450 |
-
| 4.6437 | 14400 | 0.0 | - |
|
| 451 |
-
| 4.6598 | 14450 | 0.0 | - |
|
| 452 |
-
| 4.6759 | 14500 | 0.0 | - |
|
| 453 |
-
| 4.6920 | 14550 | 0.0 | - |
|
| 454 |
-
| 4.7082 | 14600 | 0.0 | - |
|
| 455 |
-
| 4.7243 | 14650 | 0.0 | - |
|
| 456 |
-
| 4.7404 | 14700 | 0.0 | - |
|
| 457 |
-
| 4.7565 | 14750 | 0.0 | - |
|
| 458 |
-
| 4.7727 | 14800 | 0.0 | - |
|
| 459 |
-
| 4.7888 | 14850 | 0.0 | - |
|
| 460 |
-
| 4.8049 | 14900 | 0.0 | - |
|
| 461 |
-
| 4.8210 | 14950 | 0.0 | - |
|
| 462 |
-
| 4.8371 | 15000 | 0.0 | - |
|
| 463 |
-
| 4.8533 | 15050 | 0.0 | - |
|
| 464 |
-
| 4.8694 | 15100 | 0.0 | - |
|
| 465 |
-
| 4.8855 | 15150 | 0.0 | - |
|
| 466 |
-
| 4.9016 | 15200 | 0.0 | - |
|
| 467 |
-
| 4.9178 | 15250 | 0.0 | - |
|
| 468 |
-
| 4.9339 | 15300 | 0.0 | - |
|
| 469 |
-
| 4.9500 | 15350 | 0.0 | - |
|
| 470 |
-
| 4.9661 | 15400 | 0.0 | - |
|
| 471 |
-
| 4.9823 | 15450 | 0.0 | - |
|
| 472 |
-
| 4.9984 | 15500 | 0.0 | - |
|
| 473 |
-
| 5.0 | 15505 | - | 0.0259 |
|
| 474 |
-
| 5.0145 | 15550 | 0.0 | - |
|
| 475 |
-
| 5.0306 | 15600 | 0.0 | - |
|
| 476 |
-
| 5.0468 | 15650 | 0.0 | - |
|
| 477 |
-
| 5.0629 | 15700 | 0.0 | - |
|
| 478 |
-
| 5.0790 | 15750 | 0.0 | - |
|
| 479 |
-
| 5.0951 | 15800 | 0.0 | - |
|
| 480 |
-
| 5.1113 | 15850 | 0.0 | - |
|
| 481 |
-
| 5.1274 | 15900 | 0.0 | - |
|
| 482 |
-
| 5.1435 | 15950 | 0.0 | - |
|
| 483 |
-
| 5.1596 | 16000 | 0.0 | - |
|
| 484 |
-
| 5.1757 | 16050 | 0.0 | - |
|
| 485 |
-
| 5.1919 | 16100 | 0.0 | - |
|
| 486 |
-
| 5.2080 | 16150 | 0.0 | - |
|
| 487 |
-
| 5.2241 | 16200 | 0.0 | - |
|
| 488 |
-
| 5.2402 | 16250 | 0.0 | - |
|
| 489 |
-
| 5.2564 | 16300 | 0.0 | - |
|
| 490 |
-
| 5.2725 | 16350 | 0.0 | - |
|
| 491 |
-
| 5.2886 | 16400 | 0.0 | - |
|
| 492 |
-
| 5.3047 | 16450 | 0.0 | - |
|
| 493 |
-
| 5.3209 | 16500 | 0.0 | - |
|
| 494 |
-
| 5.3370 | 16550 | 0.0 | - |
|
| 495 |
-
| 5.3531 | 16600 | 0.0 | - |
|
| 496 |
-
| 5.3692 | 16650 | 0.0 | - |
|
| 497 |
-
| 5.3854 | 16700 | 0.0 | - |
|
| 498 |
-
| 5.4015 | 16750 | 0.0 | - |
|
| 499 |
-
| 5.4176 | 16800 | 0.0 | - |
|
| 500 |
-
| 5.4337 | 16850 | 0.0 | - |
|
| 501 |
-
| 5.4499 | 16900 | 0.0 | - |
|
| 502 |
-
| 5.4660 | 16950 | 0.0 | - |
|
| 503 |
-
| 5.4821 | 17000 | 0.0 | - |
|
| 504 |
-
| 5.4982 | 17050 | 0.0 | - |
|
| 505 |
-
| 5.5144 | 17100 | 0.0 | - |
|
| 506 |
-
| 5.5305 | 17150 | 0.0 | - |
|
| 507 |
-
| 5.5466 | 17200 | 0.0 | - |
|
| 508 |
-
| 5.5627 | 17250 | 0.0 | - |
|
| 509 |
-
| 5.5788 | 17300 | 0.0 | - |
|
| 510 |
-
| 5.5950 | 17350 | 0.0 | - |
|
| 511 |
-
| 5.6111 | 17400 | 0.0 | - |
|
| 512 |
-
| 5.6272 | 17450 | 0.0 | - |
|
| 513 |
-
| 5.6433 | 17500 | 0.0 | - |
|
| 514 |
-
| 5.6595 | 17550 | 0.0 | - |
|
| 515 |
-
| 5.6756 | 17600 | 0.0 | - |
|
| 516 |
-
| 5.6917 | 17650 | 0.0 | - |
|
| 517 |
-
| 5.7078 | 17700 | 0.0 | - |
|
| 518 |
-
| 5.7240 | 17750 | 0.0 | - |
|
| 519 |
-
| 5.7401 | 17800 | 0.0 | - |
|
| 520 |
-
| 5.7562 | 17850 | 0.0 | - |
|
| 521 |
-
| 5.7723 | 17900 | 0.0 | - |
|
| 522 |
-
| 5.7885 | 17950 | 0.0 | - |
|
| 523 |
-
| 5.8046 | 18000 | 0.0 | - |
|
| 524 |
-
| 5.8207 | 18050 | 0.0 | - |
|
| 525 |
-
| 5.8368 | 18100 | 0.0 | - |
|
| 526 |
-
| 5.8530 | 18150 | 0.0 | - |
|
| 527 |
-
| 5.8691 | 18200 | 0.0 | - |
|
| 528 |
-
| 5.8852 | 18250 | 0.0 | - |
|
| 529 |
-
| 5.9013 | 18300 | 0.0 | - |
|
| 530 |
-
| 5.9174 | 18350 | 0.0 | - |
|
| 531 |
-
| 5.9336 | 18400 | 0.0 | - |
|
| 532 |
-
| 5.9497 | 18450 | 0.0 | - |
|
| 533 |
-
| 5.9658 | 18500 | 0.0 | - |
|
| 534 |
-
| 5.9819 | 18550 | 0.0 | - |
|
| 535 |
-
| 5.9981 | 18600 | 0.0 | - |
|
| 536 |
-
| 6.0 | 18606 | - | 0.0255 |
|
| 537 |
-
| 6.0142 | 18650 | 0.0 | - |
|
| 538 |
-
| 6.0303 | 18700 | 0.0 | - |
|
| 539 |
-
| 6.0464 | 18750 | 0.0 | - |
|
| 540 |
-
| 6.0626 | 18800 | 0.0 | - |
|
| 541 |
-
| 6.0787 | 18850 | 0.0 | - |
|
| 542 |
-
| 6.0948 | 18900 | 0.0 | - |
|
| 543 |
-
| 6.1109 | 18950 | 0.0 | - |
|
| 544 |
-
| 6.1271 | 19000 | 0.0 | - |
|
| 545 |
-
| 6.1432 | 19050 | 0.0 | - |
|
| 546 |
-
| 6.1593 | 19100 | 0.0 | - |
|
| 547 |
-
| 6.1754 | 19150 | 0.0 | - |
|
| 548 |
-
| 6.1916 | 19200 | 0.0 | - |
|
| 549 |
-
| 6.2077 | 19250 | 0.0 | - |
|
| 550 |
-
| 6.2238 | 19300 | 0.0 | - |
|
| 551 |
-
| 6.2399 | 19350 | 0.0 | - |
|
| 552 |
-
| 6.2560 | 19400 | 0.0 | - |
|
| 553 |
-
| 6.2722 | 19450 | 0.0 | - |
|
| 554 |
-
| 6.2883 | 19500 | 0.0 | - |
|
| 555 |
-
| 6.3044 | 19550 | 0.0 | - |
|
| 556 |
-
| 6.3205 | 19600 | 0.0 | - |
|
| 557 |
-
| 6.3367 | 19650 | 0.0 | - |
|
| 558 |
-
| 6.3528 | 19700 | 0.0 | - |
|
| 559 |
-
| 6.3689 | 19750 | 0.0 | - |
|
| 560 |
-
| 6.3850 | 19800 | 0.0 | - |
|
| 561 |
-
| 6.4012 | 19850 | 0.0 | - |
|
| 562 |
-
| 6.4173 | 19900 | 0.0 | - |
|
| 563 |
-
| 6.4334 | 19950 | 0.0 | - |
|
| 564 |
-
| 6.4495 | 20000 | 0.0 | - |
|
| 565 |
-
| 6.4657 | 20050 | 0.0 | - |
|
| 566 |
-
| 6.4818 | 20100 | 0.0 | - |
|
| 567 |
-
| 6.4979 | 20150 | 0.0 | - |
|
| 568 |
-
| 6.5140 | 20200 | 0.0 | - |
|
| 569 |
-
| 6.5302 | 20250 | 0.0 | - |
|
| 570 |
-
| 6.5463 | 20300 | 0.0 | - |
|
| 571 |
-
| 6.5624 | 20350 | 0.0 | - |
|
| 572 |
-
| 6.5785 | 20400 | 0.0 | - |
|
| 573 |
-
| 6.5946 | 20450 | 0.0 | - |
|
| 574 |
-
| 6.6108 | 20500 | 0.0 | - |
|
| 575 |
-
| 6.6269 | 20550 | 0.0 | - |
|
| 576 |
-
| 6.6430 | 20600 | 0.0 | - |
|
| 577 |
-
| 6.6591 | 20650 | 0.0 | - |
|
| 578 |
-
| 6.6753 | 20700 | 0.0 | - |
|
| 579 |
-
| 6.6914 | 20750 | 0.0 | - |
|
| 580 |
-
| 6.7075 | 20800 | 0.0 | - |
|
| 581 |
-
| 6.7236 | 20850 | 0.0 | - |
|
| 582 |
-
| 6.7398 | 20900 | 0.0 | - |
|
| 583 |
-
| 6.7559 | 20950 | 0.0 | - |
|
| 584 |
-
| 6.7720 | 21000 | 0.0 | - |
|
| 585 |
-
| 6.7881 | 21050 | 0.0 | - |
|
| 586 |
-
| 6.8043 | 21100 | 0.0 | - |
|
| 587 |
-
| 6.8204 | 21150 | 0.0 | - |
|
| 588 |
-
| 6.8365 | 21200 | 0.0 | - |
|
| 589 |
-
| 6.8526 | 21250 | 0.0 | - |
|
| 590 |
-
| 6.8688 | 21300 | 0.0 | - |
|
| 591 |
-
| 6.8849 | 21350 | 0.0 | - |
|
| 592 |
-
| 6.9010 | 21400 | 0.0 | - |
|
| 593 |
-
| 6.9171 | 21450 | 0.0 | - |
|
| 594 |
-
| 6.9332 | 21500 | 0.0 | - |
|
| 595 |
-
| 6.9494 | 21550 | 0.0 | - |
|
| 596 |
-
| 6.9655 | 21600 | 0.0 | - |
|
| 597 |
-
| 6.9816 | 21650 | 0.0 | - |
|
| 598 |
-
| 6.9977 | 21700 | 0.0 | - |
|
| 599 |
-
| 7.0 | 21707 | - | 0.0264 |
|
| 600 |
-
| 7.0139 | 21750 | 0.0 | - |
|
| 601 |
-
| 7.0300 | 21800 | 0.0 | - |
|
| 602 |
-
| 7.0461 | 21850 | 0.0 | - |
|
| 603 |
-
| 7.0622 | 21900 | 0.0 | - |
|
| 604 |
-
| 7.0784 | 21950 | 0.0 | - |
|
| 605 |
-
| 7.0945 | 22000 | 0.0 | - |
|
| 606 |
-
| 7.1106 | 22050 | 0.0 | - |
|
| 607 |
-
| 7.1267 | 22100 | 0.0 | - |
|
| 608 |
-
| 7.1429 | 22150 | 0.0 | - |
|
| 609 |
-
| 7.1590 | 22200 | 0.0 | - |
|
| 610 |
-
| 7.1751 | 22250 | 0.0 | - |
|
| 611 |
-
| 7.1912 | 22300 | 0.0 | - |
|
| 612 |
-
| 7.2074 | 22350 | 0.0 | - |
|
| 613 |
-
| 7.2235 | 22400 | 0.0 | - |
|
| 614 |
-
| 7.2396 | 22450 | 0.0 | - |
|
| 615 |
-
| 7.2557 | 22500 | 0.0 | - |
|
| 616 |
-
| 7.2718 | 22550 | 0.0 | - |
|
| 617 |
-
| 7.2880 | 22600 | 0.0 | - |
|
| 618 |
-
| 7.3041 | 22650 | 0.0 | - |
|
| 619 |
-
| 7.3202 | 22700 | 0.0 | - |
|
| 620 |
-
| 7.3363 | 22750 | 0.0 | - |
|
| 621 |
-
| 7.3525 | 22800 | 0.0 | - |
|
| 622 |
-
| 7.3686 | 22850 | 0.0 | - |
|
| 623 |
-
| 7.3847 | 22900 | 0.0 | - |
|
| 624 |
-
| 7.4008 | 22950 | 0.0 | - |
|
| 625 |
-
| 7.4170 | 23000 | 0.0 | - |
|
| 626 |
-
| 7.4331 | 23050 | 0.0 | - |
|
| 627 |
-
| 7.4492 | 23100 | 0.0 | - |
|
| 628 |
-
| 7.4653 | 23150 | 0.0 | - |
|
| 629 |
-
| 7.4815 | 23200 | 0.0 | - |
|
| 630 |
-
| 7.4976 | 23250 | 0.0 | - |
|
| 631 |
-
| 7.5137 | 23300 | 0.0 | - |
|
| 632 |
-
| 7.5298 | 23350 | 0.0 | - |
|
| 633 |
-
| 7.5460 | 23400 | 0.0 | - |
|
| 634 |
-
| 7.5621 | 23450 | 0.0 | - |
|
| 635 |
-
| 7.5782 | 23500 | 0.0 | - |
|
| 636 |
-
| 7.5943 | 23550 | 0.0 | - |
|
| 637 |
-
| 7.6104 | 23600 | 0.0 | - |
|
| 638 |
-
| 7.6266 | 23650 | 0.0 | - |
|
| 639 |
-
| 7.6427 | 23700 | 0.0 | - |
|
| 640 |
-
| 7.6588 | 23750 | 0.0 | - |
|
| 641 |
-
| 7.6749 | 23800 | 0.0 | - |
|
| 642 |
-
| 7.6911 | 23850 | 0.0 | - |
|
| 643 |
-
| 7.7072 | 23900 | 0.0 | - |
|
| 644 |
-
| 7.7233 | 23950 | 0.0 | - |
|
| 645 |
-
| 7.7394 | 24000 | 0.0 | - |
|
| 646 |
-
| 7.7556 | 24050 | 0.0 | - |
|
| 647 |
-
| 7.7717 | 24100 | 0.0 | - |
|
| 648 |
-
| 7.7878 | 24150 | 0.0 | - |
|
| 649 |
-
| 7.8039 | 24200 | 0.0 | - |
|
| 650 |
-
| 7.8201 | 24250 | 0.0 | - |
|
| 651 |
-
| 7.8362 | 24300 | 0.0 | - |
|
| 652 |
-
| 7.8523 | 24350 | 0.0 | - |
|
| 653 |
-
| 7.8684 | 24400 | 0.0 | - |
|
| 654 |
-
| 7.8846 | 24450 | 0.0 | - |
|
| 655 |
-
| 7.9007 | 24500 | 0.0 | - |
|
| 656 |
-
| 7.9168 | 24550 | 0.0 | - |
|
| 657 |
-
| 7.9329 | 24600 | 0.0 | - |
|
| 658 |
-
| 7.9490 | 24650 | 0.0 | - |
|
| 659 |
-
| 7.9652 | 24700 | 0.0 | - |
|
| 660 |
-
| 7.9813 | 24750 | 0.0 | - |
|
| 661 |
-
| 7.9974 | 24800 | 0.0 | - |
|
| 662 |
-
| 8.0 | 24808 | - | 0.0252 |
|
| 663 |
-
| 8.0135 | 24850 | 0.0 | - |
|
| 664 |
-
| 8.0297 | 24900 | 0.0 | - |
|
| 665 |
-
| 8.0458 | 24950 | 0.0 | - |
|
| 666 |
-
| 8.0619 | 25000 | 0.0 | - |
|
| 667 |
-
| 8.0780 | 25050 | 0.0 | - |
|
| 668 |
-
| 8.0942 | 25100 | 0.0 | - |
|
| 669 |
-
| 8.1103 | 25150 | 0.0 | - |
|
| 670 |
-
| 8.1264 | 25200 | 0.0 | - |
|
| 671 |
-
| 8.1425 | 25250 | 0.0 | - |
|
| 672 |
-
| 8.1587 | 25300 | 0.0 | - |
|
| 673 |
-
| 8.1748 | 25350 | 0.0 | - |
|
| 674 |
-
| 8.1909 | 25400 | 0.0 | - |
|
| 675 |
-
| 8.2070 | 25450 | 0.0 | - |
|
| 676 |
-
| 8.2232 | 25500 | 0.0 | - |
|
| 677 |
-
| 8.2393 | 25550 | 0.0 | - |
|
| 678 |
-
| 8.2554 | 25600 | 0.0 | - |
|
| 679 |
-
| 8.2715 | 25650 | 0.0 | - |
|
| 680 |
-
| 8.2876 | 25700 | 0.0 | - |
|
| 681 |
-
| 8.3038 | 25750 | 0.0 | - |
|
| 682 |
-
| 8.3199 | 25800 | 0.0 | - |
|
| 683 |
-
| 8.3360 | 25850 | 0.0 | - |
|
| 684 |
-
| 8.3521 | 25900 | 0.0 | - |
|
| 685 |
-
| 8.3683 | 25950 | 0.0 | - |
|
| 686 |
-
| 8.3844 | 26000 | 0.0 | - |
|
| 687 |
-
| 8.4005 | 26050 | 0.0 | - |
|
| 688 |
-
| 8.4166 | 26100 | 0.0 | - |
|
| 689 |
-
| 8.4328 | 26150 | 0.0 | - |
|
| 690 |
-
| 8.4489 | 26200 | 0.0 | - |
|
| 691 |
-
| 8.4650 | 26250 | 0.0 | - |
|
| 692 |
-
| 8.4811 | 26300 | 0.0 | - |
|
| 693 |
-
| 8.4973 | 26350 | 0.0 | - |
|
| 694 |
-
| 8.5134 | 26400 | 0.0 | - |
|
| 695 |
-
| 8.5295 | 26450 | 0.0 | - |
|
| 696 |
-
| 8.5456 | 26500 | 0.0 | - |
|
| 697 |
-
| 8.5618 | 26550 | 0.0 | - |
|
| 698 |
-
| 8.5779 | 26600 | 0.0 | - |
|
| 699 |
-
| 8.5940 | 26650 | 0.0 | - |
|
| 700 |
-
| 8.6101 | 26700 | 0.0 | - |
|
| 701 |
-
| 8.6262 | 26750 | 0.0 | - |
|
| 702 |
-
| 8.6424 | 26800 | 0.0 | - |
|
| 703 |
-
| 8.6585 | 26850 | 0.0 | - |
|
| 704 |
-
| 8.6746 | 26900 | 0.0 | - |
|
| 705 |
-
| 8.6907 | 26950 | 0.0 | - |
|
| 706 |
-
| 8.7069 | 27000 | 0.0 | - |
|
| 707 |
-
| 8.7230 | 27050 | 0.0 | - |
|
| 708 |
-
| 8.7391 | 27100 | 0.0 | - |
|
| 709 |
-
| 8.7552 | 27150 | 0.0 | - |
|
| 710 |
-
| 8.7714 | 27200 | 0.0 | - |
|
| 711 |
-
| 8.7875 | 27250 | 0.0 | - |
|
| 712 |
-
| 8.8036 | 27300 | 0.0 | - |
|
| 713 |
-
| 8.8197 | 27350 | 0.0 | - |
|
| 714 |
-
| 8.8359 | 27400 | 0.0 | - |
|
| 715 |
-
| 8.8520 | 27450 | 0.0 | - |
|
| 716 |
-
| 8.8681 | 27500 | 0.0 | - |
|
| 717 |
-
| 8.8842 | 27550 | 0.0 | - |
|
| 718 |
-
| 8.9004 | 27600 | 0.0 | - |
|
| 719 |
-
| 8.9165 | 27650 | 0.0 | - |
|
| 720 |
-
| 8.9326 | 27700 | 0.0 | - |
|
| 721 |
-
| 8.9487 | 27750 | 0.0 | - |
|
| 722 |
-
| 8.9649 | 27800 | 0.0 | - |
|
| 723 |
-
| 8.9810 | 27850 | 0.0 | - |
|
| 724 |
-
| 8.9971 | 27900 | 0.0 | - |
|
| 725 |
-
| 9.0 | 27909 | - | 0.0255 |
|
| 726 |
-
| 9.0132 | 27950 | 0.0 | - |
|
| 727 |
-
| 9.0293 | 28000 | 0.0 | - |
|
| 728 |
-
| 9.0455 | 28050 | 0.0 | - |
|
| 729 |
-
| 9.0616 | 28100 | 0.0 | - |
|
| 730 |
-
| 9.0777 | 28150 | 0.0 | - |
|
| 731 |
-
| 9.0938 | 28200 | 0.0 | - |
|
| 732 |
-
| 9.1100 | 28250 | 0.0 | - |
|
| 733 |
-
| 9.1261 | 28300 | 0.0 | - |
|
| 734 |
-
| 9.1422 | 28350 | 0.0 | - |
|
| 735 |
-
| 9.1583 | 28400 | 0.0 | - |
|
| 736 |
-
| 9.1745 | 28450 | 0.0 | - |
|
| 737 |
-
| 9.1906 | 28500 | 0.0 | - |
|
| 738 |
-
| 9.2067 | 28550 | 0.0 | - |
|
| 739 |
-
| 9.2228 | 28600 | 0.0 | - |
|
| 740 |
-
| 9.2390 | 28650 | 0.0 | - |
|
| 741 |
-
| 9.2551 | 28700 | 0.0 | - |
|
| 742 |
-
| 9.2712 | 28750 | 0.0 | - |
|
| 743 |
-
| 9.2873 | 28800 | 0.0 | - |
|
| 744 |
-
| 9.3035 | 28850 | 0.0 | - |
|
| 745 |
-
| 9.3196 | 28900 | 0.0 | - |
|
| 746 |
-
| 9.3357 | 28950 | 0.0 | - |
|
| 747 |
-
| 9.3518 | 29000 | 0.0 | - |
|
| 748 |
-
| 9.3679 | 29050 | 0.0 | - |
|
| 749 |
-
| 9.3841 | 29100 | 0.0 | - |
|
| 750 |
-
| 9.4002 | 29150 | 0.0 | - |
|
| 751 |
-
| 9.4163 | 29200 | 0.0 | - |
|
| 752 |
-
| 9.4324 | 29250 | 0.0 | - |
|
| 753 |
-
| 9.4486 | 29300 | 0.0 | - |
|
| 754 |
-
| 9.4647 | 29350 | 0.0 | - |
|
| 755 |
-
| 9.4808 | 29400 | 0.0 | - |
|
| 756 |
-
| 9.4969 | 29450 | 0.0 | - |
|
| 757 |
-
| 9.5131 | 29500 | 0.0 | - |
|
| 758 |
-
| 9.5292 | 29550 | 0.0 | - |
|
| 759 |
-
| 9.5453 | 29600 | 0.0 | - |
|
| 760 |
-
| 9.5614 | 29650 | 0.0 | - |
|
| 761 |
-
| 9.5776 | 29700 | 0.0 | - |
|
| 762 |
-
| 9.5937 | 29750 | 0.0 | - |
|
| 763 |
-
| 9.6098 | 29800 | 0.0 | - |
|
| 764 |
-
| 9.6259 | 29850 | 0.0 | - |
|
| 765 |
-
| 9.6421 | 29900 | 0.0 | - |
|
| 766 |
-
| 9.6582 | 29950 | 0.0 | - |
|
| 767 |
-
| 9.6743 | 30000 | 0.0 | - |
|
| 768 |
-
| 9.6904 | 30050 | 0.0 | - |
|
| 769 |
-
| 9.7065 | 30100 | 0.0 | - |
|
| 770 |
-
| 9.7227 | 30150 | 0.0 | - |
|
| 771 |
-
| 9.7388 | 30200 | 0.0 | - |
|
| 772 |
-
| 9.7549 | 30250 | 0.0 | - |
|
| 773 |
-
| 9.7710 | 30300 | 0.0 | - |
|
| 774 |
-
| 9.7872 | 30350 | 0.0 | - |
|
| 775 |
-
| 9.8033 | 30400 | 0.0 | - |
|
| 776 |
-
| 9.8194 | 30450 | 0.0 | - |
|
| 777 |
-
| 9.8355 | 30500 | 0.0 | - |
|
| 778 |
-
| 9.8517 | 30550 | 0.0 | - |
|
| 779 |
-
| 9.8678 | 30600 | 0.0 | - |
|
| 780 |
-
| 9.8839 | 30650 | 0.0 | - |
|
| 781 |
-
| 9.9000 | 30700 | 0.0 | - |
|
| 782 |
-
| 9.9162 | 30750 | 0.0 | - |
|
| 783 |
-
| 9.9323 | 30800 | 0.0 | - |
|
| 784 |
-
| 9.9484 | 30850 | 0.0 | - |
|
| 785 |
-
| 9.9645 | 30900 | 0.0 | - |
|
| 786 |
-
| 9.9807 | 30950 | 0.0 | - |
|
| 787 |
-
| 9.9968 | 31000 | 0.0 | - |
|
| 788 |
-
| 10.0 | 31010 | - | 0.0264 |
|
| 789 |
-
|
| 790 |
-
* The bold row denotes the saved checkpoint.
|
| 791 |
-
### Framework Versions
|
| 792 |
-
- Python: 3.9.18
|
| 793 |
-
- SetFit: 1.0.3
|
| 794 |
-
- Sentence Transformers: 2.2.1
|
| 795 |
-
- Transformers: 4.32.1
|
| 796 |
-
- PyTorch: 1.10.0
|
| 797 |
-
- Datasets: 2.20.0
|
| 798 |
-
- Tokenizers: 0.13.3
|
| 799 |
-
|
| 800 |
-
## Citation
|
| 801 |
-
|
| 802 |
-
### BibTeX
|
| 803 |
-
```bibtex
|
| 804 |
-
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 805 |
-
doi = {10.48550/ARXIV.2209.11055},
|
| 806 |
-
url = {https://arxiv.org/abs/2209.11055},
|
| 807 |
-
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 808 |
-
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 809 |
-
title = {Efficient Few-Shot Learning Without Prompts},
|
| 810 |
-
publisher = {arXiv},
|
| 811 |
-
year = {2022},
|
| 812 |
-
copyright = {Creative Commons Attribution 4.0 International}
|
| 813 |
-
}
|
| 814 |
-
```
|
| 815 |
-
|
| 816 |
-
<!--
|
| 817 |
-
## Glossary
|
| 818 |
-
|
| 819 |
-
*Clearly define terms in order to be accessible across audiences.*
|
| 820 |
-
-->
|
| 821 |
-
|
| 822 |
-
<!--
|
| 823 |
-
## Model Card Authors
|
| 824 |
-
|
| 825 |
-
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 826 |
-
-->
|
| 827 |
-
|
| 828 |
-
<!--
|
| 829 |
-
## Model Card Contact
|
| 830 |
-
|
| 831 |
-
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 832 |
-
-->
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