AceVikings/deberta-misconception-classifier

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-large5tags:6- generated_from_trainer7metrics:8- accuracy9model-index:10- name: deberta-misconception-classifier11  results: []12---13 14<!-- This model card has been generated automatically according to the information the Trainer had access to. You15should probably proofread and complete it, then remove this comment. -->16 17# deberta-misconception-classifier18 19This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.20It achieves the following results on the evaluation set:21- Loss: 0.259522- Macro F1: 0.601223- Weighted F1: 0.786224- Accuracy: 0.782325- Map@3: 0.884626 27## Model description28 29More information needed30 31## Intended uses & limitations32 33More information needed34 35## Training and evaluation data36 37More information needed38 39## Training procedure40 41### Training hyperparameters42 43The following hyperparameters were used during training:44- learning_rate: 1e-0545- train_batch_size: 846- eval_batch_size: 847- seed: 4248- gradient_accumulation_steps: 249- total_train_batch_size: 1650- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments51- lr_scheduler_type: linear52- num_epochs: 1053- mixed_precision_training: Native AMP54 55### Training results56 57| Training Loss | Epoch  | Step | Validation Loss | Macro F1 | Weighted F1 | Accuracy | Map@3  |58|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:------:|59| 1.3548        | 0.2422 | 500  | 1.0357          | 0.2067   | 0.4193      | 0.4221   | 0.5941 |60| 0.9062        | 0.4845 | 1000 | 0.7145          | 0.3536   | 0.6222      | 0.6183   | 0.7672 |61| 0.5924        | 0.7267 | 1500 | 0.4780          | 0.4251   | 0.7250      | 0.7368   | 0.8460 |62| 0.4113        | 0.9690 | 2000 | 0.4354          | 0.4210   | 0.7139      | 0.7354   | 0.8430 |63| 0.2906        | 1.2112 | 2500 | 0.3885          | 0.4757   | 0.7373      | 0.7559   | 0.8635 |64| 0.3248        | 1.4535 | 3000 | 0.3100          | 0.5215   | 0.7591      | 0.7589   | 0.8651 |65| 0.264         | 1.6957 | 3500 | 0.3245          | 0.5371   | 0.7838      | 0.7864   | 0.8852 |66| 0.3461        | 1.9380 | 4000 | 0.2863          | 0.5582   | 0.8036      | 0.8136   | 0.8988 |67| 0.202         | 2.1802 | 4500 | 0.2697          | 0.5758   | 0.8058      | 0.8147   | 0.9013 |68| 0.1641        | 2.4225 | 5000 | 0.2837          | 0.6015   | 0.8224      | 0.8245   | 0.9062 |69| 0.1642        | 2.6647 | 5500 | 0.2991          | 0.5559   | 0.8113      | 0.8139   | 0.9009 |70| 0.1857        | 2.9070 | 6000 | 0.2518          | 0.5931   | 0.8051      | 0.8109   | 0.8995 |71| 0.1322        | 3.1492 | 6500 | 0.2595          | 0.6012   | 0.7862      | 0.7823   | 0.8846 |72 73 74### Framework versions75 76- Transformers 4.53.377- Pytorch 2.6.0+cu12478- Datasets 4.0.079- Tokenizers 0.21.280