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Add SetFit model

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Files changed (9) hide show
  1. README.md +679 -279
  2. config.json +1 -1
  3. config_sentence_transformers.json +2 -2
  4. config_setfit.json +7 -4
  5. model_head.pkl +1 -1
  6. pytorch_model.bin +1 -1
  7. sentence_bert_config.json +1 -1
  8. tokenizer.json +1 -1
  9. tokenizer_config.json +1 -1
README.md CHANGED
@@ -1,5 +1,4 @@
1
  ---
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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
@@ -17,7 +16,7 @@ widget:
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  - text: ๋ธ”๋ž™ํ™€ ์ •๋ณด ์—ญ์„ค์— ๋Œ€ํ•ด ์„ค๋ช…ํ•œ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ํฌ์ธํŠธ๋ฅผ ์งง๊ฒŒ ์ง‘์•ฝํ•ด ์ค„๋ž˜?
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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
@@ -32,9 +31,9 @@ model-index:
32
  name: Accuracy
33
  ---
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35
- # SetFit with jhgan/ko-sroberta-multitask
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37
- This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jhgan/ko-sroberta-multitask](https://huggingface.co/jhgan/ko-sroberta-multitask) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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  The model has been trained using an efficient few-shot learning technique that involves:
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@@ -45,9 +44,9 @@ The model has been trained using an efficient few-shot learning technique that i
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46
  ### 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:** 5 classes
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  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
53
  <!-- - **Language:** Unknown -->
@@ -127,14 +126,17 @@ preds = model("๊ธฐ์—…์ด ํŒŒ์‚ฐ ์‹ ์ฒญ์„ ํ•  ๋•Œ ์ฑ„๋ฌด์ž์˜ ์ฃผ๋œ ์ฑ…์ž„ ๋ฒ”
127
  |:-------------|:----|:--------|:----|
128
  | Word count | 6 | 12.4219 | 27 |
129
 
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- | Label | Training Sample Count |
131
- |:--------|:----------------------|
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- | rag | 0 |
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- | general | 0 |
 
 
 
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  ### Training Hyperparameters
136
  - 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)
@@ -150,274 +152,672 @@ preds = model("๊ธฐ์—…์ด ํŒŒ์‚ฐ ์‹ ์ฒญ์„ ํ•  ๋•Œ ์ฑ„๋ฌด์ž์˜ ์ฃผ๋œ ์ฑ…์ž„ ๋ฒ”
150
  - load_best_model_at_end: True
151
 
152
  ### Training Results
153
- | Epoch | Step | Training Loss | Validation Loss |
154
- |:-------:|:---------:|:-------------:|:---------------:|
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- | 0.0003 | 1 | 0.1889 | - |
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- | 0.0153 | 50 | 0.1818 | - |
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- | 0.0306 | 100 | 0.1421 | - |
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- | 0.0459 | 150 | 0.0582 | - |
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- | 0.0612 | 200 | 0.0299 | - |
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- | 0.0765 | 250 | 0.0093 | - |
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- | 0.0918 | 300 | 0.0036 | - |
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- | 0.1071 | 350 | 0.001 | - |
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- | 0.1224 | 400 | 0.0012 | - |
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- | 0.1377 | 450 | 0.0006 | - |
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- | 0.1530 | 500 | 0.0006 | - |
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- | 0.1683 | 550 | 0.0003 | - |
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- | 0.1836 | 600 | 0.0003 | - |
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- | 0.1989 | 650 | 0.0003 | - |
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- | 0.2142 | 700 | 0.0002 | - |
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- | 0.2295 | 750 | 0.0002 | - |
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- | 0.2448 | 800 | 0.0002 | - |
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- | 0.2601 | 850 | 0.0001 | - |
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- | 0.2754 | 900 | 0.0001 | - |
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- | 0.2907 | 950 | 0.0001 | - |
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- | 0.3060 | 1000 | 0.0001 | - |
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- | 0.3213 | 1050 | 0.0001 | - |
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- | 0.3366 | 1100 | 0.0001 | - |
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- | 0.3519 | 1150 | 0.0001 | - |
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- | 0.3672 | 1200 | 0.0001 | - |
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- | 0.3825 | 1250 | 0.0001 | - |
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- | 0.3978 | 1300 | 0.0001 | - |
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- | 0.4131 | 1350 | 0.0001 | - |
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- | 0.4284 | 1400 | 0.0001 | - |
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- | 0.4437 | 1450 | 0.0001 | - |
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- | 0.4590 | 1500 | 0.0 | - |
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- | 0.4743 | 1550 | 0.0001 | - |
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- | 0.4896 | 1600 | 0.0 | - |
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- | 0.5049 | 1650 | 0.0001 | - |
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- | 0.5202 | 1700 | 0.0 | - |
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- | 0.5355 | 1750 | 0.0 | - |
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- | 0.5508 | 1800 | 0.0 | - |
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- | 0.5661 | 1850 | 0.0 | - |
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- | 0.5814 | 1900 | 0.0 | - |
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- | 0.5967 | 1950 | 0.0 | - |
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- | 0.6120 | 2000 | 0.0 | - |
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- | 0.6273 | 2050 | 0.0 | - |
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- | 0.6426 | 2100 | 0.0 | - |
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- | 0.6579 | 2150 | 0.0 | - |
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- | 0.6732 | 2200 | 0.0 | - |
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- | 0.6885 | 2250 | 0.0 | - |
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- | 0.7038 | 2300 | 0.0 | - |
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- | 0.7191 | 2350 | 0.0 | - |
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- | 0.7344 | 2400 | 0.0 | - |
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- | 0.7497 | 2450 | 0.0 | - |
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- | 0.7650 | 2500 | 0.0 | - |
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- | 0.7803 | 2550 | 0.0 | - |
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- | 0.7956 | 2600 | 0.0 | - |
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- | 0.8109 | 2650 | 0.0 | - |
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- | 0.8262 | 2700 | 0.0 | - |
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- | 0.8415 | 2750 | 0.0 | - |
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- | 0.8568 | 2800 | 0.0 | - |
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- | 0.8721 | 2850 | 0.0 | - |
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- | 0.8874 | 2900 | 0.0 | - |
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- | 0.9027 | 2950 | 0.0 | - |
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- | 0.9180 | 3000 | 0.0 | - |
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- | 0.9333 | 3050 | 0.0 | - |
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- | 0.9486 | 3100 | 0.0 | - |
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- | 0.9639 | 3150 | 0.0 | - |
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- | 0.9792 | 3200 | 0.0 | - |
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- | 0.9945 | 3250 | 0.0 | - |
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- | 1.0 | 3268 | - | 0.0497 |
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- | 1.0098 | 3300 | 0.0 | - |
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- | 1.0251 | 3350 | 0.0 | - |
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- | 1.0404 | 3400 | 0.0 | - |
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- | 1.0557 | 3450 | 0.0 | - |
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- | 1.0710 | 3500 | 0.0 | - |
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- | 1.0863 | 3550 | 0.0 | - |
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- | 1.1016 | 3600 | 0.0 | - |
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- | 1.1169 | 3650 | 0.0 | - |
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- | 1.1322 | 3700 | 0.0 | - |
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- | 1.1475 | 3750 | 0.0 | - |
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- | 1.1628 | 3800 | 0.0 | - |
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- | 1.1781 | 3850 | 0.0 | - |
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- | 1.1934 | 3900 | 0.0 | - |
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- | 1.2087 | 3950 | 0.0 | - |
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- | 1.2240 | 4000 | 0.0 | - |
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- | 1.2393 | 4050 | 0.0 | - |
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- | 1.2546 | 4100 | 0.0 | - |
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- | 1.2699 | 4150 | 0.0 | - |
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- | 1.2852 | 4200 | 0.0 | - |
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- | 1.3005 | 4250 | 0.0 | - |
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- | 1.3158 | 4300 | 0.0 | - |
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- | 1.3311 | 4350 | 0.0 | - |
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- | 1.3464 | 4400 | 0.0 | - |
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- | 1.3617 | 4450 | 0.0 | - |
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- | 1.3770 | 4500 | 0.0 | - |
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- | 1.3923 | 4550 | 0.0 | - |
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- | 1.4076 | 4600 | 0.0 | - |
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- | 1.4229 | 4650 | 0.0 | - |
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- | 1.4382 | 4700 | 0.0 | - |
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- | 1.4535 | 4750 | 0.0 | - |
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- | 1.4688 | 4800 | 0.0 | - |
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- | 1.4841 | 4850 | 0.0 | - |
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- | 1.4994 | 4900 | 0.0 | - |
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- | 1.5147 | 4950 | 0.0 | - |
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- | 1.5300 | 5000 | 0.0 | - |
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- | 1.5453 | 5050 | 0.0 | - |
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- | 1.5606 | 5100 | 0.0 | - |
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- | 1.5759 | 5150 | 0.0 | - |
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- | 1.5912 | 5200 | 0.0 | - |
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- | 1.6065 | 5250 | 0.0 | - |
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- | 1.6218 | 5300 | 0.0 | - |
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- | 1.6371 | 5350 | 0.0 | - |
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- | 1.6524 | 5400 | 0.0 | - |
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- | 1.6677 | 5450 | 0.0 | - |
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- | 1.6830 | 5500 | 0.0 | - |
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- | 1.6983 | 5550 | 0.0 | - |
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- | 1.7136 | 5600 | 0.0 | - |
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- | 1.7289 | 5650 | 0.0 | - |
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- | 1.7442 | 5700 | 0.0 | - |
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- | 1.7595 | 5750 | 0.0 | - |
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- | 1.7748 | 5800 | 0.0 | - |
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- | 1.7901 | 5850 | 0.0 | - |
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- | 1.8054 | 5900 | 0.0 | - |
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- | 1.8207 | 5950 | 0.0 | - |
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- | 1.8360 | 6000 | 0.0 | - |
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- | 1.8513 | 6050 | 0.0 | - |
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- | 1.8666 | 6100 | 0.0 | - |
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- | 1.8819 | 6150 | 0.0 | - |
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- | 1.8972 | 6200 | 0.0 | - |
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- | 1.9125 | 6250 | 0.0 | - |
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- | 1.9278 | 6300 | 0.0 | - |
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- | 1.9431 | 6350 | 0.0 | - |
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- | 1.9584 | 6400 | 0.0 | - |
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- | 1.9737 | 6450 | 0.0 | - |
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- | 1.9890 | 6500 | 0.0 | - |
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- | 2.0 | 6536 | - | 0.056 |
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- | 2.0043 | 6550 | 0.0 | - |
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- | 2.0196 | 6600 | 0.0 | - |
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- | 2.0349 | 6650 | 0.0 | - |
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- | 2.0502 | 6700 | 0.0 | - |
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- | 2.0655 | 6750 | 0.0 | - |
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- | 2.0808 | 6800 | 0.0 | - |
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- | 2.0961 | 6850 | 0.0 | - |
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- | 2.1114 | 6900 | 0.0 | - |
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- | 2.1267 | 6950 | 0.0 | - |
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- | 2.1420 | 7000 | 0.0 | - |
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- | 2.1573 | 7050 | 0.0 | - |
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- | 2.1726 | 7100 | 0.0 | - |
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- | 2.1879 | 7150 | 0.0 | - |
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- | 2.2032 | 7200 | 0.0 | - |
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- | 2.2185 | 7250 | 0.0 | - |
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- | 2.2338 | 7300 | 0.0 | - |
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- | 2.2491 | 7350 | 0.0 | - |
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- | 2.2644 | 7400 | 0.0 | - |
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- | 2.2797 | 7450 | 0.0 | - |
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- | 2.2950 | 7500 | 0.0 | - |
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- | 2.3103 | 7550 | 0.0 | - |
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- | 2.3256 | 7600 | 0.0 | - |
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- | 2.3409 | 7650 | 0.0 | - |
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- | 2.3562 | 7700 | 0.0 | - |
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- | 2.3715 | 7750 | 0.0 | - |
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- | 2.3868 | 7800 | 0.0 | - |
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- | 2.4021 | 7850 | 0.0 | - |
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- | 2.4174 | 7900 | 0.0 | - |
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- | 2.4327 | 7950 | 0.0 | - |
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- | 2.4480 | 8000 | 0.0 | - |
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- | 2.4633 | 8050 | 0.0 | - |
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- | 2.4786 | 8100 | 0.0 | - |
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- | 2.4939 | 8150 | 0.0 | - |
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- | 2.5092 | 8200 | 0.0 | - |
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- | 2.5245 | 8250 | 0.0 | - |
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- | 2.5398 | 8300 | 0.0 | - |
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- | 2.5551 | 8350 | 0.0 | - |
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- | 2.5704 | 8400 | 0.0 | - |
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- | 2.5857 | 8450 | 0.0 | - |
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- | 2.6010 | 8500 | 0.0 | - |
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- | 2.6163 | 8550 | 0.0 | - |
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- | 2.6316 | 8600 | 0.0 | - |
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- | 2.6469 | 8650 | 0.0 | - |
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- | 2.6622 | 8700 | 0.0 | - |
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- | 2.6775 | 8750 | 0.0 | - |
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- | 2.6928 | 8800 | 0.0 | - |
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- | 2.7081 | 8850 | 0.0 | - |
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- | 2.7234 | 8900 | 0.0 | - |
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- | 2.7387 | 8950 | 0.0 | - |
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- | 2.7540 | 9000 | 0.0 | - |
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- | 2.7693 | 9050 | 0.0 | - |
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- | 2.7846 | 9100 | 0.0 | - |
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- | 2.7999 | 9150 | 0.0 | - |
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- | 2.8152 | 9200 | 0.0 | - |
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- | 2.8305 | 9250 | 0.0 | - |
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- | 2.8458 | 9300 | 0.0 | - |
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- | 2.8611 | 9350 | 0.0 | - |
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- | 2.8764 | 9400 | 0.0 | - |
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- | 2.8917 | 9450 | 0.0 | - |
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- | 2.9070 | 9500 | 0.0 | - |
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- | 2.9223 | 9550 | 0.0 | - |
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- | 2.9376 | 9600 | 0.0 | - |
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- | 2.9529 | 9650 | 0.0 | - |
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- | 2.9682 | 9700 | 0.0 | - |
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- | 2.9835 | 9750 | 0.0 | - |
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- | 2.9988 | 9800 | 0.0 | - |
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- | 3.0 | 9804 | - | 0.061 |
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- | 3.0141 | 9850 | 0.0 | - |
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- | 3.0294 | 9900 | 0.0 | - |
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- | 3.0447 | 9950 | 0.0 | - |
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- | 3.0600 | 10000 | 0.0 | - |
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- | 3.0753 | 10050 | 0.0 | - |
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- | 3.0906 | 10100 | 0.0 | - |
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- | 3.1059 | 10150 | 0.0 | - |
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- | 3.1212 | 10200 | 0.0 | - |
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- | 3.1365 | 10250 | 0.0 | - |
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- | 3.1518 | 10300 | 0.0 | - |
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- | 3.1671 | 10350 | 0.0 | - |
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- | 3.1824 | 10400 | 0.0 | - |
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- | 3.1977 | 10450 | 0.0 | - |
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- | 3.2130 | 10500 | 0.0 | - |
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- | 3.2283 | 10550 | 0.0 | - |
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- | 3.2436 | 10600 | 0.0 | - |
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- | 3.2589 | 10650 | 0.0 | - |
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- | 3.2742 | 10700 | 0.0 | - |
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- | 3.2895 | 10750 | 0.0 | - |
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- | 3.3048 | 10800 | 0.0 | - |
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- | 3.3201 | 10850 | 0.0 | - |
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- | 3.3354 | 10900 | 0.0 | - |
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- | 3.3507 | 10950 | 0.0 | - |
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- | 3.3660 | 11000 | 0.0 | - |
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- | 3.3813 | 11050 | 0.0 | - |
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- | 3.3966 | 11100 | 0.0 | - |
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- | 3.4119 | 11150 | 0.0 | - |
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- | 3.4272 | 11200 | 0.0001 | - |
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- | 3.4425 | 11250 | 0.0 | - |
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- | 3.4578 | 11300 | 0.0 | - |
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- | 3.4731 | 11350 | 0.0 | - |
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- | 3.4884 | 11400 | 0.0 | - |
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- | 3.5037 | 11450 | 0.0 | - |
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- | 3.5190 | 11500 | 0.0 | - |
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- | 3.5343 | 11550 | 0.0 | - |
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- | 3.5496 | 11600 | 0.0 | - |
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- | 3.5649 | 11650 | 0.0 | - |
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- | 3.5802 | 11700 | 0.0 | - |
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- | 3.5955 | 11750 | 0.0 | - |
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- | 3.6108 | 11800 | 0.0 | - |
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- | 3.6261 | 11850 | 0.0 | - |
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- | 3.6414 | 11900 | 0.0 | - |
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- | 3.6567 | 11950 | 0.0 | - |
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- | 3.6720 | 12000 | 0.0 | - |
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- | 3.6873 | 12050 | 0.0 | - |
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- | 3.7026 | 12100 | 0.0 | - |
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- | 3.7179 | 12150 | 0.0 | - |
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- | 3.7332 | 12200 | 0.0 | - |
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- | 3.7485 | 12250 | 0.0 | - |
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- | 3.7638 | 12300 | 0.0 | - |
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- | 3.7791 | 12350 | 0.0 | - |
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- | 3.7944 | 12400 | 0.0 | - |
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- | 3.8097 | 12450 | 0.0 | - |
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- | 3.8250 | 12500 | 0.0 | - |
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- | 3.8403 | 12550 | 0.0 | - |
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- | 3.8556 | 12600 | 0.0 | - |
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- | 3.8709 | 12650 | 0.0 | - |
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- | 3.8862 | 12700 | 0.0 | - |
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- | 3.9015 | 12750 | 0.0 | - |
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- | 3.9168 | 12800 | 0.0 | - |
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- | 3.9321 | 12850 | 0.0 | - |
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- | 3.9474 | 12900 | 0.0 | - |
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- | 3.9627 | 12950 | 0.0 | - |
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- | 3.9780 | 13000 | 0.0 | - |
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- | 3.9933 | 13050 | 0.0 | - |
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- | **4.0** | **13072** | **-** | **0.0479** |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
421
 
422
  * The bold row denotes the saved checkpoint.
423
  ### Framework Versions
 
1
  ---
 
2
  library_name: setfit
3
  metrics:
4
  - accuracy
 
16
  - text: ๋ธ”๋ž™ํ™€ ์ •๋ณด ์—ญ์„ค์— ๋Œ€ํ•ด ์„ค๋ช…ํ•œ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ํฌ์ธํŠธ๋ฅผ ์งง๊ฒŒ ์ง‘์•ฝํ•ด ์ค„๋ž˜?
17
  inference: true
18
  model-index:
19
+ - name: SetFit
20
  results:
21
  - task:
22
  type: text-classification
 
31
  name: Accuracy
32
  ---
33
 
34
+ # SetFit
35
 
36
+ 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.
37
 
38
  The model has been trained using an efficient few-shot learning technique that involves:
39
 
 
44
 
45
  ### Model Description
46
  - **Model Type:** SetFit
47
+ <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
48
  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
49
+ - **Maximum Sequence Length:** 512 tokens
50
  - **Number of Classes:** 5 classes
51
  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
52
  <!-- - **Language:** Unknown -->
 
126
  |:-------------|:----|:--------|:----|
127
  | Word count | 6 | 12.4219 | 27 |
128
 
129
+ | Label | Training Sample Count |
130
+ |:-----------|:----------------------|
131
+ | ์š”์•ฝ | 105 |
132
+ | ์ค‘๋ณต์„ฑ ๊ฒ€ํ†  | 83 |
133
+ | ํŠนํ™” ์ง€์‹์ •๋ณด ์ œ๊ณต | 109 |
134
+ | ์œ ์‚ฌ๋ฌธ์„œ | 115 |
135
+ | ์˜คํƒˆ์ž ํƒ์ง€ | 100 |
136
 
137
  ### Training Hyperparameters
138
  - batch_size: (64, 64)
139
+ - num_epochs: (10, 10)
140
  - max_steps: -1
141
  - sampling_strategy: oversampling
142
  - body_learning_rate: (2e-05, 1e-05)
 
152
  - load_best_model_at_end: True
153
 
154
  ### Training Results
155
+ | Epoch | Step | Training Loss | Validation Loss |
156
+ |:-------:|:--------:|:-------------:|:---------------:|
157
+ | 0.0003 | 1 | 0.2208 | - |
158
+ | 0.0153 | 50 | 0.239 | - |
159
+ | 0.0306 | 100 | 0.2042 | - |
160
+ | 0.0459 | 150 | 0.1802 | - |
161
+ | 0.0612 | 200 | 0.1299 | - |
162
+ | 0.0765 | 250 | 0.0869 | - |
163
+ | 0.0918 | 300 | 0.0524 | - |
164
+ | 0.1071 | 350 | 0.0206 | - |
165
+ | 0.1224 | 400 | 0.0248 | - |
166
+ | 0.1377 | 450 | 0.0054 | - |
167
+ | 0.1530 | 500 | 0.0043 | - |
168
+ | 0.1683 | 550 | 0.0021 | - |
169
+ | 0.1836 | 600 | 0.0021 | - |
170
+ | 0.1989 | 650 | 0.0009 | - |
171
+ | 0.2142 | 700 | 0.0009 | - |
172
+ | 0.2295 | 750 | 0.0006 | - |
173
+ | 0.2448 | 800 | 0.0005 | - |
174
+ | 0.2601 | 850 | 0.0004 | - |
175
+ | 0.2754 | 900 | 0.0004 | - |
176
+ | 0.2907 | 950 | 0.0003 | - |
177
+ | 0.3060 | 1000 | 0.0003 | - |
178
+ | 0.3213 | 1050 | 0.0003 | - |
179
+ | 0.3366 | 1100 | 0.0002 | - |
180
+ | 0.3519 | 1150 | 0.0002 | - |
181
+ | 0.3672 | 1200 | 0.0002 | - |
182
+ | 0.3825 | 1250 | 0.0002 | - |
183
+ | 0.3978 | 1300 | 0.0002 | - |
184
+ | 0.4131 | 1350 | 0.0002 | - |
185
+ | 0.4284 | 1400 | 0.0001 | - |
186
+ | 0.4437 | 1450 | 0.0001 | - |
187
+ | 0.4590 | 1500 | 0.0001 | - |
188
+ | 0.4743 | 1550 | 0.0001 | - |
189
+ | 0.4896 | 1600 | 0.0001 | - |
190
+ | 0.5049 | 1650 | 0.0001 | - |
191
+ | 0.5202 | 1700 | 0.0001 | - |
192
+ | 0.5355 | 1750 | 0.0001 | - |
193
+ | 0.5508 | 1800 | 0.0001 | - |
194
+ | 0.5661 | 1850 | 0.0001 | - |
195
+ | 0.5814 | 1900 | 0.0001 | - |
196
+ | 0.5967 | 1950 | 0.0001 | - |
197
+ | 0.6120 | 2000 | 0.0001 | - |
198
+ | 0.6273 | 2050 | 0.0001 | - |
199
+ | 0.6426 | 2100 | 0.0001 | - |
200
+ | 0.6579 | 2150 | 0.0001 | - |
201
+ | 0.6732 | 2200 | 0.0001 | - |
202
+ | 0.6885 | 2250 | 0.0001 | - |
203
+ | 0.7038 | 2300 | 0.0001 | - |
204
+ | 0.7191 | 2350 | 0.0001 | - |
205
+ | 0.7344 | 2400 | 0.0 | - |
206
+ | 0.7497 | 2450 | 0.0001 | - |
207
+ | 0.7650 | 2500 | 0.0 | - |
208
+ | 0.7803 | 2550 | 0.0001 | - |
209
+ | 0.7956 | 2600 | 0.0 | - |
210
+ | 0.8109 | 2650 | 0.0001 | - |
211
+ | 0.8262 | 2700 | 0.0 | - |
212
+ | 0.8415 | 2750 | 0.0001 | - |
213
+ | 0.8568 | 2800 | 0.0 | - |
214
+ | 0.8721 | 2850 | 0.0 | - |
215
+ | 0.8874 | 2900 | 0.0 | - |
216
+ | 0.9027 | 2950 | 0.0 | - |
217
+ | 0.9180 | 3000 | 0.0 | - |
218
+ | 0.9333 | 3050 | 0.0001 | - |
219
+ | 0.9486 | 3100 | 0.0 | - |
220
+ | 0.9639 | 3150 | 0.0 | - |
221
+ | 0.9792 | 3200 | 0.0 | - |
222
+ | 0.9945 | 3250 | 0.0 | - |
223
+ | **1.0** | **3268** | **-** | **0.0462** |
224
+ | 1.0098 | 3300 | 0.0 | - |
225
+ | 1.0251 | 3350 | 0.0 | - |
226
+ | 1.0404 | 3400 | 0.0 | - |
227
+ | 1.0557 | 3450 | 0.0 | - |
228
+ | 1.0710 | 3500 | 0.0 | - |
229
+ | 1.0863 | 3550 | 0.0 | - |
230
+ | 1.1016 | 3600 | 0.0 | - |
231
+ | 1.1169 | 3650 | 0.0 | - |
232
+ | 1.1322 | 3700 | 0.0 | - |
233
+ | 1.1475 | 3750 | 0.0 | - |
234
+ | 1.1628 | 3800 | 0.0 | - |
235
+ | 1.1781 | 3850 | 0.0 | - |
236
+ | 1.1934 | 3900 | 0.0 | - |
237
+ | 1.2087 | 3950 | 0.0 | - |
238
+ | 1.2240 | 4000 | 0.0 | - |
239
+ | 1.2393 | 4050 | 0.0 | - |
240
+ | 1.2546 | 4100 | 0.0 | - |
241
+ | 1.2699 | 4150 | 0.0 | - |
242
+ | 1.2852 | 4200 | 0.0 | - |
243
+ | 1.3005 | 4250 | 0.0 | - |
244
+ | 1.3158 | 4300 | 0.0 | - |
245
+ | 1.3311 | 4350 | 0.0 | - |
246
+ | 1.3464 | 4400 | 0.0 | - |
247
+ | 1.3617 | 4450 | 0.0 | - |
248
+ | 1.3770 | 4500 | 0.0 | - |
249
+ | 1.3923 | 4550 | 0.0 | - |
250
+ | 1.4076 | 4600 | 0.0 | - |
251
+ | 1.4229 | 4650 | 0.0 | - |
252
+ | 1.4382 | 4700 | 0.0 | - |
253
+ | 1.4535 | 4750 | 0.0 | - |
254
+ | 1.4688 | 4800 | 0.0 | - |
255
+ | 1.4841 | 4850 | 0.0001 | - |
256
+ | 1.4994 | 4900 | 0.0 | - |
257
+ | 1.5147 | 4950 | 0.0 | - |
258
+ | 1.5300 | 5000 | 0.0 | - |
259
+ | 1.5453 | 5050 | 0.0 | - |
260
+ | 1.5606 | 5100 | 0.0 | - |
261
+ | 1.5759 | 5150 | 0.0 | - |
262
+ | 1.5912 | 5200 | 0.0 | - |
263
+ | 1.6065 | 5250 | 0.0 | - |
264
+ | 1.6218 | 5300 | 0.0 | - |
265
+ | 1.6371 | 5350 | 0.0 | - |
266
+ | 1.6524 | 5400 | 0.0 | - |
267
+ | 1.6677 | 5450 | 0.0 | - |
268
+ | 1.6830 | 5500 | 0.0 | - |
269
+ | 1.6983 | 5550 | 0.0 | - |
270
+ | 1.7136 | 5600 | 0.0 | - |
271
+ | 1.7289 | 5650 | 0.0 | - |
272
+ | 1.7442 | 5700 | 0.0 | - |
273
+ | 1.7595 | 5750 | 0.0 | - |
274
+ | 1.7748 | 5800 | 0.0 | - |
275
+ | 1.7901 | 5850 | 0.0 | - |
276
+ | 1.8054 | 5900 | 0.0 | - |
277
+ | 1.8207 | 5950 | 0.0 | - |
278
+ | 1.8360 | 6000 | 0.0 | - |
279
+ | 1.8513 | 6050 | 0.0 | - |
280
+ | 1.8666 | 6100 | 0.0 | - |
281
+ | 1.8819 | 6150 | 0.0 | - |
282
+ | 1.8972 | 6200 | 0.0 | - |
283
+ | 1.9125 | 6250 | 0.0 | - |
284
+ | 1.9278 | 6300 | 0.0 | - |
285
+ | 1.9431 | 6350 | 0.0 | - |
286
+ | 1.9584 | 6400 | 0.0 | - |
287
+ | 1.9737 | 6450 | 0.0 | - |
288
+ | 1.9890 | 6500 | 0.0 | - |
289
+ | 2.0 | 6536 | - | 0.0473 |
290
+ | 2.0043 | 6550 | 0.0 | - |
291
+ | 2.0196 | 6600 | 0.0 | - |
292
+ | 2.0349 | 6650 | 0.0 | - |
293
+ | 2.0502 | 6700 | 0.0 | - |
294
+ | 2.0655 | 6750 | 0.0 | - |
295
+ | 2.0808 | 6800 | 0.0 | - |
296
+ | 2.0961 | 6850 | 0.0 | - |
297
+ | 2.1114 | 6900 | 0.0 | - |
298
+ | 2.1267 | 6950 | 0.0 | - |
299
+ | 2.1420 | 7000 | 0.0 | - |
300
+ | 2.1573 | 7050 | 0.0 | - |
301
+ | 2.1726 | 7100 | 0.0 | - |
302
+ | 2.1879 | 7150 | 0.0 | - |
303
+ | 2.2032 | 7200 | 0.0 | - |
304
+ | 2.2185 | 7250 | 0.0 | - |
305
+ | 2.2338 | 7300 | 0.0 | - |
306
+ | 2.2491 | 7350 | 0.0 | - |
307
+ | 2.2644 | 7400 | 0.0 | - |
308
+ | 2.2797 | 7450 | 0.0 | - |
309
+ | 2.2950 | 7500 | 0.0 | - |
310
+ | 2.3103 | 7550 | 0.0 | - |
311
+ | 2.3256 | 7600 | 0.0 | - |
312
+ | 2.3409 | 7650 | 0.0 | - |
313
+ | 2.3562 | 7700 | 0.0 | - |
314
+ | 2.3715 | 7750 | 0.0 | - |
315
+ | 2.3868 | 7800 | 0.0 | - |
316
+ | 2.4021 | 7850 | 0.0 | - |
317
+ | 2.4174 | 7900 | 0.0 | - |
318
+ | 2.4327 | 7950 | 0.0 | - |
319
+ | 2.4480 | 8000 | 0.0 | - |
320
+ | 2.4633 | 8050 | 0.0 | - |
321
+ | 2.4786 | 8100 | 0.0 | - |
322
+ | 2.4939 | 8150 | 0.0 | - |
323
+ | 2.5092 | 8200 | 0.0 | - |
324
+ | 2.5245 | 8250 | 0.0 | - |
325
+ | 2.5398 | 8300 | 0.0 | - |
326
+ | 2.5551 | 8350 | 0.0 | - |
327
+ | 2.5704 | 8400 | 0.0 | - |
328
+ | 2.5857 | 8450 | 0.0 | - |
329
+ | 2.6010 | 8500 | 0.0 | - |
330
+ | 2.6163 | 8550 | 0.0 | - |
331
+ | 2.6316 | 8600 | 0.0 | - |
332
+ | 2.6469 | 8650 | 0.0 | - |
333
+ | 2.6622 | 8700 | 0.0 | - |
334
+ | 2.6775 | 8750 | 0.0 | - |
335
+ | 2.6928 | 8800 | 0.0 | - |
336
+ | 2.7081 | 8850 | 0.0 | - |
337
+ | 2.7234 | 8900 | 0.0 | - |
338
+ | 2.7387 | 8950 | 0.0 | - |
339
+ | 2.7540 | 9000 | 0.0 | - |
340
+ | 2.7693 | 9050 | 0.0 | - |
341
+ | 2.7846 | 9100 | 0.0 | - |
342
+ | 2.7999 | 9150 | 0.0 | - |
343
+ | 2.8152 | 9200 | 0.0 | - |
344
+ | 2.8305 | 9250 | 0.0 | - |
345
+ | 2.8458 | 9300 | 0.0 | - |
346
+ | 2.8611 | 9350 | 0.0 | - |
347
+ | 2.8764 | 9400 | 0.0 | - |
348
+ | 2.8917 | 9450 | 0.0 | - |
349
+ | 2.9070 | 9500 | 0.0 | - |
350
+ | 2.9223 | 9550 | 0.0 | - |
351
+ | 2.9376 | 9600 | 0.0 | - |
352
+ | 2.9529 | 9650 | 0.0 | - |
353
+ | 2.9682 | 9700 | 0.0 | - |
354
+ | 2.9835 | 9750 | 0.0 | - |
355
+ | 2.9988 | 9800 | 0.0 | - |
356
+ | 3.0 | 9804 | - | 0.0483 |
357
+ | 3.0141 | 9850 | 0.0 | - |
358
+ | 3.0294 | 9900 | 0.0 | - |
359
+ | 3.0447 | 9950 | 0.0 | - |
360
+ | 3.0600 | 10000 | 0.0 | - |
361
+ | 3.0753 | 10050 | 0.0 | - |
362
+ | 3.0906 | 10100 | 0.0 | - |
363
+ | 3.1059 | 10150 | 0.0 | - |
364
+ | 3.1212 | 10200 | 0.0 | - |
365
+ | 3.1365 | 10250 | 0.0 | - |
366
+ | 3.1518 | 10300 | 0.0 | - |
367
+ | 3.1671 | 10350 | 0.0 | - |
368
+ | 3.1824 | 10400 | 0.0 | - |
369
+ | 3.1977 | 10450 | 0.0 | - |
370
+ | 3.2130 | 10500 | 0.0 | - |
371
+ | 3.2283 | 10550 | 0.0 | - |
372
+ | 3.2436 | 10600 | 0.0 | - |
373
+ | 3.2589 | 10650 | 0.0 | - |
374
+ | 3.2742 | 10700 | 0.0 | - |
375
+ | 3.2895 | 10750 | 0.0 | - |
376
+ | 3.3048 | 10800 | 0.0 | - |
377
+ | 3.3201 | 10850 | 0.0 | - |
378
+ | 3.3354 | 10900 | 0.0 | - |
379
+ | 3.3507 | 10950 | 0.0 | - |
380
+ | 3.3660 | 11000 | 0.0 | - |
381
+ | 3.3813 | 11050 | 0.0 | - |
382
+ | 3.3966 | 11100 | 0.0 | - |
383
+ | 3.4119 | 11150 | 0.0 | - |
384
+ | 3.4272 | 11200 | 0.0 | - |
385
+ | 3.4425 | 11250 | 0.0 | - |
386
+ | 3.4578 | 11300 | 0.0 | - |
387
+ | 3.4731 | 11350 | 0.0 | - |
388
+ | 3.4884 | 11400 | 0.0 | - |
389
+ | 3.5037 | 11450 | 0.0 | - |
390
+ | 3.5190 | 11500 | 0.0 | - |
391
+ | 3.5343 | 11550 | 0.0 | - |
392
+ | 3.5496 | 11600 | 0.0 | - |
393
+ | 3.5649 | 11650 | 0.0 | - |
394
+ | 3.5802 | 11700 | 0.0 | - |
395
+ | 3.5955 | 11750 | 0.0 | - |
396
+ | 3.6108 | 11800 | 0.0 | - |
397
+ | 3.6261 | 11850 | 0.0 | - |
398
+ | 3.6414 | 11900 | 0.0 | - |
399
+ | 3.6567 | 11950 | 0.0 | - |
400
+ | 3.6720 | 12000 | 0.0 | - |
401
+ | 3.6873 | 12050 | 0.0 | - |
402
+ | 3.7026 | 12100 | 0.0 | - |
403
+ | 3.7179 | 12150 | 0.0005 | - |
404
+ | 3.7332 | 12200 | 0.0001 | - |
405
+ | 3.7485 | 12250 | 0.0 | - |
406
+ | 3.7638 | 12300 | 0.0 | - |
407
+ | 3.7791 | 12350 | 0.0 | - |
408
+ | 3.7944 | 12400 | 0.0 | - |
409
+ | 3.8097 | 12450 | 0.0 | - |
410
+ | 3.8250 | 12500 | 0.0 | - |
411
+ | 3.8403 | 12550 | 0.0 | - |
412
+ | 3.8556 | 12600 | 0.0 | - |
413
+ | 3.8709 | 12650 | 0.0 | - |
414
+ | 3.8862 | 12700 | 0.0 | - |
415
+ | 3.9015 | 12750 | 0.0 | - |
416
+ | 3.9168 | 12800 | 0.0 | - |
417
+ | 3.9321 | 12850 | 0.0 | - |
418
+ | 3.9474 | 12900 | 0.0 | - |
419
+ | 3.9627 | 12950 | 0.0 | - |
420
+ | 3.9780 | 13000 | 0.0 | - |
421
+ | 3.9933 | 13050 | 0.0 | - |
422
+ | 4.0 | 13072 | - | 0.071 |
423
+ | 4.0086 | 13100 | 0.0 | - |
424
+ | 4.0239 | 13150 | 0.0 | - |
425
+ | 4.0392 | 13200 | 0.0 | - |
426
+ | 4.0545 | 13250 | 0.0 | - |
427
+ | 4.0698 | 13300 | 0.0 | - |
428
+ | 4.0851 | 13350 | 0.0 | - |
429
+ | 4.1004 | 13400 | 0.0 | - |
430
+ | 4.1157 | 13450 | 0.0 | - |
431
+ | 4.1310 | 13500 | 0.0 | - |
432
+ | 4.1463 | 13550 | 0.0 | - |
433
+ | 4.1616 | 13600 | 0.0 | - |
434
+ | 4.1769 | 13650 | 0.0 | - |
435
+ | 4.1922 | 13700 | 0.0 | - |
436
+ | 4.2075 | 13750 | 0.0 | - |
437
+ | 4.2228 | 13800 | 0.0 | - |
438
+ | 4.2381 | 13850 | 0.0 | - |
439
+ | 4.2534 | 13900 | 0.0 | - |
440
+ | 4.2687 | 13950 | 0.0 | - |
441
+ | 4.2840 | 14000 | 0.0 | - |
442
+ | 4.2993 | 14050 | 0.0 | - |
443
+ | 4.3146 | 14100 | 0.0 | - |
444
+ | 4.3299 | 14150 | 0.0 | - |
445
+ | 4.3452 | 14200 | 0.0 | - |
446
+ | 4.3605 | 14250 | 0.0 | - |
447
+ | 4.3758 | 14300 | 0.0 | - |
448
+ | 4.3911 | 14350 | 0.0 | - |
449
+ | 4.4064 | 14400 | 0.0 | - |
450
+ | 4.4217 | 14450 | 0.0 | - |
451
+ | 4.4370 | 14500 | 0.0 | - |
452
+ | 4.4523 | 14550 | 0.0 | - |
453
+ | 4.4676 | 14600 | 0.0 | - |
454
+ | 4.4829 | 14650 | 0.0 | - |
455
+ | 4.4982 | 14700 | 0.0 | - |
456
+ | 4.5135 | 14750 | 0.0 | - |
457
+ | 4.5288 | 14800 | 0.0 | - |
458
+ | 4.5441 | 14850 | 0.0 | - |
459
+ | 4.5594 | 14900 | 0.0 | - |
460
+ | 4.5747 | 14950 | 0.0 | - |
461
+ | 4.5900 | 15000 | 0.0 | - |
462
+ | 4.6053 | 15050 | 0.0 | - |
463
+ | 4.6206 | 15100 | 0.0 | - |
464
+ | 4.6359 | 15150 | 0.0 | - |
465
+ | 4.6512 | 15200 | 0.0 | - |
466
+ | 4.6665 | 15250 | 0.0 | - |
467
+ | 4.6818 | 15300 | 0.0 | - |
468
+ | 4.6971 | 15350 | 0.0 | - |
469
+ | 4.7124 | 15400 | 0.0 | - |
470
+ | 4.7277 | 15450 | 0.0 | - |
471
+ | 4.7430 | 15500 | 0.0 | - |
472
+ | 4.7583 | 15550 | 0.0 | - |
473
+ | 4.7736 | 15600 | 0.0 | - |
474
+ | 4.7889 | 15650 | 0.0 | - |
475
+ | 4.8042 | 15700 | 0.0 | - |
476
+ | 4.8195 | 15750 | 0.0 | - |
477
+ | 4.8348 | 15800 | 0.0 | - |
478
+ | 4.8501 | 15850 | 0.0 | - |
479
+ | 4.8654 | 15900 | 0.0 | - |
480
+ | 4.8807 | 15950 | 0.0 | - |
481
+ | 4.8960 | 16000 | 0.0 | - |
482
+ | 4.9113 | 16050 | 0.0 | - |
483
+ | 4.9266 | 16100 | 0.0 | - |
484
+ | 4.9419 | 16150 | 0.0 | - |
485
+ | 4.9572 | 16200 | 0.0 | - |
486
+ | 4.9725 | 16250 | 0.0 | - |
487
+ | 4.9878 | 16300 | 0.0 | - |
488
+ | 5.0 | 16340 | - | 0.0718 |
489
+ | 5.0031 | 16350 | 0.0 | - |
490
+ | 5.0184 | 16400 | 0.0 | - |
491
+ | 5.0337 | 16450 | 0.0 | - |
492
+ | 5.0490 | 16500 | 0.0 | - |
493
+ | 5.0643 | 16550 | 0.0 | - |
494
+ | 5.0796 | 16600 | 0.0 | - |
495
+ | 5.0949 | 16650 | 0.0 | - |
496
+ | 5.1102 | 16700 | 0.0 | - |
497
+ | 5.1255 | 16750 | 0.0 | - |
498
+ | 5.1408 | 16800 | 0.0 | - |
499
+ | 5.1561 | 16850 | 0.0 | - |
500
+ | 5.1714 | 16900 | 0.0 | - |
501
+ | 5.1867 | 16950 | 0.0 | - |
502
+ | 5.2020 | 17000 | 0.0 | - |
503
+ | 5.2173 | 17050 | 0.0 | - |
504
+ | 5.2326 | 17100 | 0.0 | - |
505
+ | 5.2479 | 17150 | 0.0 | - |
506
+ | 5.2632 | 17200 | 0.0 | - |
507
+ | 5.2785 | 17250 | 0.0 | - |
508
+ | 5.2938 | 17300 | 0.0 | - |
509
+ | 5.3091 | 17350 | 0.0 | - |
510
+ | 5.3244 | 17400 | 0.0 | - |
511
+ | 5.3397 | 17450 | 0.0 | - |
512
+ | 5.3550 | 17500 | 0.0 | - |
513
+ | 5.3703 | 17550 | 0.0 | - |
514
+ | 5.3856 | 17600 | 0.0 | - |
515
+ | 5.4009 | 17650 | 0.0 | - |
516
+ | 5.4162 | 17700 | 0.0 | - |
517
+ | 5.4315 | 17750 | 0.0 | - |
518
+ | 5.4468 | 17800 | 0.0 | - |
519
+ | 5.4621 | 17850 | 0.0 | - |
520
+ | 5.4774 | 17900 | 0.0 | - |
521
+ | 5.4927 | 17950 | 0.0 | - |
522
+ | 5.5080 | 18000 | 0.0 | - |
523
+ | 5.5233 | 18050 | 0.0 | - |
524
+ | 5.5386 | 18100 | 0.0 | - |
525
+ | 5.5539 | 18150 | 0.0 | - |
526
+ | 5.5692 | 18200 | 0.0 | - |
527
+ | 5.5845 | 18250 | 0.0 | - |
528
+ | 5.5998 | 18300 | 0.0 | - |
529
+ | 5.6151 | 18350 | 0.0 | - |
530
+ | 5.6304 | 18400 | 0.0 | - |
531
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822
  * The bold row denotes the saved checkpoint.
823
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