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Token Classification
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roberta
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End of training

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  1. README.md +30 -30
  2. model.safetensors +1 -1
  3. runs/Feb26_07-51-22_8e685f45cf65/events.out.tfevents.1708933883.8e685f45cf65.55830.13 +3 -0
  4. runs/Feb26_07-57-59_8e685f45cf65/events.out.tfevents.1708934281.8e685f45cf65.55830.14 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -15,29 +15,29 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.9474
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- - Overall Precision: 0.0909
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- - Overall Recall: 0.0570
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- - Overall F1: 0.0701
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- - Overall Accuracy: 0.1682
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- - 材質 Precision: 0.1020
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- - 材質 Recall: 0.0746
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- - 材質 F1: 0.0862
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- - フルート Precision: 0.0423
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- - フルート Recall: 0.0390
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- - フルート F1: 0.0405
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- - 寸法 Precision: 0.0625
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- - 寸法 Recall: 0.0159
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- - 寸法 F1: 0.0253
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- - シートのカット寸法 Precision: 0.1091
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- - シートのカット寸法 Recall: 0.0845
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- - シートのカット寸法 F1: 0.0952
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- - 数量 Precision: 0.1029
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- - 数量 Recall: 0.1077
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- - 数量 F1: 0.1053
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- - 納期 Precision: 0.4
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- - 納期 Recall: 0.0256
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- - 納期 F1: 0.0482
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  ## Model description
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@@ -57,8 +57,8 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 6
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- - eval_batch_size: 2
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
@@ -66,11 +66,11 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | 材質 Precision | 材質 Recall | 材質 F1 | フルート Precision | フルート Recall | フルート F1 | 寸法 Precision | 寸法 Recall | 寸法 F1 | シートのカット寸法 Precision | シートのカット寸法 Recall | シートのカット寸法 F1 | 数量 Precision | 数量 Recall | 数量 F1 | 納期 Precision | 納期 Recall | 納期 F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:---------:|:------:|:--------------:|:-----------:|:-------:|:------------:|:---------:|:------:|:-------------------:|:----------------:|:------------:|:------------:|:---------:|:------:|:------------:|:---------:|:------:|
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- | No log | 1.0 | 1 | 1.9496 | 0.0631 | 0.0333 | 0.0435 | 0.1646 | 0.0426 | 0.0299 | 0.0351 | 0.0759 | 0.0779 | 0.0769 | 0.0370 | 0.0159 | 0.0222 | 0.1 | 0.0423 | 0.0594 | 0.0526 | 0.0308 | 0.0388 | 0.0 | 0.0 | 0.0 |
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- | No log | 2.0 | 2 | 1.9488 | 0.0691 | 0.0451 | 0.0546 | 0.1627 | 0.0962 | 0.0746 | 0.0840 | 0.0361 | 0.0390 | 0.0375 | 0.0556 | 0.0159 | 0.0247 | 0.0909 | 0.0704 | 0.0794 | 0.0615 | 0.0615 | 0.0615 | 0.5 | 0.0128 | 0.025 |
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- | No log | 3.0 | 3 | 1.9474 | 0.0909 | 0.0570 | 0.0701 | 0.1682 | 0.1020 | 0.0746 | 0.0862 | 0.0423 | 0.0390 | 0.0405 | 0.0625 | 0.0159 | 0.0253 | 0.1091 | 0.0845 | 0.0952 | 0.1029 | 0.1077 | 0.1053 | 0.4 | 0.0256 | 0.0482 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.8668
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+ - Overall Precision: 0.0
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+ - Overall Recall: 0.0
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+ - Overall F1: 0.0
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+ - Overall Accuracy: 0.32
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+ - 材質 Precision: 0.0
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+ - 材質 Recall: 0.0
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+ - 材質 F1: 0.0
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+ - フルート Precision: 0.0
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+ - フルート Recall: 0.0
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+ - フルート F1: 0.0
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+ - 寸法 Precision: 0.0
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+ - 寸法 Recall: 0.0
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+ - 寸法 F1: 0.0
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+ - シートのカット寸法 Precision: 0.0
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+ - シートのカット寸法 Recall: 0.0
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+ - シートのカット寸法 F1: 0.0
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+ - 数量 Precision: 0.0
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+ - 数量 Recall: 0.0
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+ - 数量 F1: 0.0
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+ - 納期 Precision: 0.0
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+ - 納期 Recall: 0.0
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+ - 納期 F1: 0.0
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 1
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | 材質 Precision | 材質 Recall | 材質 F1 | フルート Precision | フルート Recall | フルート F1 | 寸法 Precision | 寸法 Recall | 寸法 F1 | シートのカット寸法 Precision | シートのカット寸法 Recall | シートのカット寸法 F1 | 数量 Precision | 数量 Recall | 数量 F1 | 納期 Precision | 納期 Recall | 納期 F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:---------:|:-----:|:--------------:|:-----------:|:-------:|:------------:|:---------:|:-----:|:-------------------:|:----------------:|:------------:|:------------:|:---------:|:-----:|:------------:|:---------:|:-----:|
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+ | No log | 1.0 | 1 | 1.8901 | 0.0 | 0.0 | 0.0 | 0.32 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8384 | 2.0 | 2 | 1.8728 | 0.0 | 0.0 | 0.0 | 0.32 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8384 | 3.0 | 3 | 1.8668 | 0.0 | 0.0 | 0.0 | 0.32 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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  ### Framework versions
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