End of training

README.md

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+---
+base_model: Fsoft-AIC/videberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: ComOM-VIDeBERTa-1
+  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. -->
+
+# ComOM-VIDeBERTa-1
+
+This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4882
+- Accuracy: 0.7737
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 156  | 0.5779          | 0.6790   |
+| No log        | 2.0   | 312  | 0.5388          | 0.6934   |
+| No log        | 3.0   | 468  | 0.4926          | 0.7576   |
+| 0.5564        | 4.0   | 624  | 0.5177          | 0.7705   |
+| 0.5564        | 5.0   | 780  | 0.4882          | 0.7737   |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.14.1

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