BERRAMOU/camembert-math-classification

1---2library_name: transformers3license: mit4base_model: camembert-base5tags:6- generated_from_trainer7metrics:8- accuracy9model-index:10- name: camembert-math-classification11  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# camembert-math-classification18 19This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.20It achieves the following results on the evaluation set:21- Loss: 0.016622- F1 Macro: 0.998723- F1 Weighted: 0.998724- F1 Correct: 1.025- F1 Partiel: 0.998026- F1 Incorrect: 0.998027- Accuracy: 0.998728 29## Model description30 31More information needed32 33## Intended uses & limitations34 35More information needed36 37## Training and evaluation data38 39More information needed40 41## Training procedure42 43### Training hyperparameters44 45The following hyperparameters were used during training:46- learning_rate: 2e-0547- train_batch_size: 3248- eval_batch_size: 6449- seed: 4250- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments51- lr_scheduler_type: linear52- lr_scheduler_warmup_steps: 0.153- num_epochs: 554- mixed_precision_training: Native AMP55 56### Training results57 58| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 Correct | F1 Partiel | F1 Incorrect | Accuracy |59|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:----------:|:----------:|:------------:|:--------:|60| 0.6561        | 1.0   | 184  | 0.8086          | 0.4912   | 0.4912      | 0.8169     | 0.0204     | 0.6364       | 0.5675   |61| 0.1997        | 2.0   | 368  | 0.1530          | 0.9669   | 0.9669      | 0.9902     | 0.9513     | 0.9592       | 0.9669   |62| 0.0648        | 3.0   | 552  | 0.0360          | 0.9960   | 0.9960      | 1.0        | 0.9940     | 0.9941       | 0.9960   |63| 0.0261        | 4.0   | 736  | 0.0227          | 0.9974   | 0.9974      | 0.9980     | 0.9960     | 0.9980       | 0.9974   |64| 0.0147        | 5.0   | 920  | 0.0166          | 0.9987   | 0.9987      | 1.0        | 0.9980     | 0.9980       | 0.9987   |65 66 67### Framework versions68 69- Transformers 5.0.070- Pytorch 2.10.0+cu12871- Datasets 5.0.072- Tokenizers 0.22.273