Model save

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- recall
+model-index:
+- name: bert-base-cased_K5
+  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. -->
+
+# bert-base-cased_K5
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0247
+- F1 Macro: 0.9972
+- F1: 0.9981
+- F1 Neg: 0.9964
+- Acc: 0.9975
+- Prec: 1.0
+- Recall: 0.9962
+- Mcc: 0.9945
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1     | F1 Neg | Acc    | Prec   | Recall | Mcc    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:|
+| No log        | 1.0   | 400  | 0.0454          | 0.9931   | 0.9952 | 0.9909 | 0.9938 | 0.9962 | 0.9943 | 0.9862 |
+| 0.0631        | 2.0   | 800  | 0.0334          | 0.9931   | 0.9952 | 0.9909 | 0.9938 | 0.9943 | 0.9962 | 0.9861 |
+| 0.0275        | 3.0   | 1200 | 0.0422          | 0.9945   | 0.9962 | 0.9928 | 0.995  | 1.0    | 0.9924 | 0.9890 |
+| 0.0073        | 4.0   | 1600 | 0.0504          | 0.9931   | 0.9952 | 0.9910 | 0.9938 | 1.0    | 0.9905 | 0.9863 |
+| 0.0042        | 5.0   | 2000 | 0.0247          | 0.9972   | 0.9981 | 0.9964 | 0.9975 | 1.0    | 0.9962 | 0.9945 |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2