RonTon05/multi_task_model_content

1---2library_name: peft3license: agpl-3.04base_model: RonTon05/model_content_V2_test5tags:6- lora7metrics:8- accuracy9- f110model-index:11- name: multi_task_model_content12  results: []13---14 15<!-- This model card has been generated automatically according to the information the Trainer had access to. You16should probably proofread and complete it, then remove this comment. -->17 18# multi_task_model_content19 20This model is a fine-tuned version of [RonTon05/model_content_V2_test](https://huggingface.co/RonTon05/model_content_V2_test) on the None dataset.21It achieves the following results on the evaluation set:22- Loss: 1.775323- Accuracy: 0.475324- F1: 0.509925 26## Model description27 28More information needed29 30## Intended uses & limitations31 32More information needed33 34## Training and evaluation data35 36More information needed37 38## Training procedure39 40### Training hyperparameters41 42The following hyperparameters were used during training:43- learning_rate: 0.000144- train_batch_size: 6445- eval_batch_size: 6446- seed: 4247- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments48- lr_scheduler_type: linear49- num_epochs: 1050 51### Training results52 53| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |54|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|55| 2.0293        | 1.0   | 330  | 1.9741          | 0.2832   | 0.1707 |56| 1.9647        | 2.0   | 660  | 1.9200          | 0.3949   | 0.2933 |57| 1.9221        | 3.0   | 990  | 1.8736          | 0.4171   | 0.3336 |58| 1.8875        | 4.0   | 1320 | 1.8436          | 0.4211   | 0.3419 |59| 1.8633        | 5.0   | 1650 | 1.8225          | 0.4380   | 0.3739 |60| 1.8451        | 6.0   | 1980 | 1.8029          | 0.4596   | 0.4718 |61| 1.8321        | 7.0   | 2310 | 1.7910          | 0.4717   | 0.5035 |62| 1.8214        | 8.0   | 2640 | 1.7819          | 0.4710   | 0.4954 |63| 1.8139        | 9.0   | 2970 | 1.7764          | 0.4744   | 0.5062 |64| 1.8101        | 10.0  | 3300 | 1.7753          | 0.4753   | 0.5099 |65 66 67### Framework versions68 69- PEFT 0.16.070- Transformers 4.57.171- Pytorch 2.6.0+cu12472- Datasets 4.4.173- Tokenizers 0.22.1