Model save

README.md

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+---
+library_name: transformers
+base_model: Hartunka/distilbert_km_100_v2
+tags:
+- generated_from_trainer
+metrics:
+- matthews_correlation
+- accuracy
+model-index:
+- name: distilbert_km_100_v2_cola
+  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. -->
+
+# distilbert_km_100_v2_cola
+
+This model is a fine-tuned version of [Hartunka/distilbert_km_100_v2](https://huggingface.co/Hartunka/distilbert_km_100_v2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8187
+- Matthews Correlation: 0.0553
+- Accuracy: 0.6261
+
+## 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: 5e-05
+- train_batch_size: 256
+- eval_batch_size: 256
+- seed: 10
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
+| 0.6184        | 1.0   | 34   | 0.6179          | 0.0                  | 0.6913   |
+| 0.5955        | 2.0   | 68   | 0.6295          | 0.0041               | 0.6894   |
+| 0.5633        | 3.0   | 102  | 0.6458          | 0.0414               | 0.6817   |
+| 0.5103        | 4.0   | 136  | 0.7045          | 0.0628               | 0.6414   |
+| 0.4488        | 5.0   | 170  | 0.7053          | 0.0752               | 0.6472   |
+| 0.3769        | 6.0   | 204  | 0.8187          | 0.0553               | 0.6261   |
+
+
+### Framework versions
+
+- Transformers 4.50.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.21.1

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