End of training

README.md

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+---
+license: mit
+base_model: rinna/japanese-roberta-base
+tags:
+- generated_from_trainer
+model-index:
+- name: test
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# test
+
+This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.9474
+- Overall Precision: 0.0909
+- Overall Recall: 0.0570
+- Overall F1: 0.0701
+- Overall Accuracy: 0.1682
+- 材質 Precision: 0.1020
+- 材質 Recall: 0.0746
+- 材質 F1: 0.0862
+- フルート Precision: 0.0423
+- フルート Recall: 0.0390
+- フルート F1: 0.0405
+- 寸法 Precision: 0.0625
+- 寸法 Recall: 0.0159
+- 寸法 F1: 0.0253
+- シートのカット寸法 Precision: 0.1091
+- シートのカット寸法 Recall: 0.0845
+- シートのカット寸法 F1: 0.0952
+- 数量 Precision: 0.1029
+- 数量 Recall: 0.1077
+- 数量 F1: 0.1053
+- 納期 Precision: 0.4
+- 納期 Recall: 0.0256
+- 納期 F1: 0.0482
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 6
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| 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  |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:---------:|:------:|:--------------:|:-----------:|:-------:|:------------:|:---------:|:------:|:-------------------:|:----------------:|:------------:|:------------:|:---------:|:------:|:------------:|:---------:|:------:|
+| 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    |
+| 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  |
+| 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 |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

config.json

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+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 3,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

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