--- license: apache-2.0 base_model: facebook/convnextv2-pico-1k-224 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: 10-convnextv2-pico-1k-224-finetuned-spiderTraining20-500 results: [] --- # 10-convnextv2-pico-1k-224-finetuned-spiderTraining20-500 This model is a fine-tuned version of [facebook/convnextv2-pico-1k-224](https://huggingface.co./facebook/convnextv2-pico-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - Accuracy: 0.8949 - Precision: 0.8942 - Recall: 0.8897 - F1: 0.8904 ## 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: 25 - eval_batch_size: 25 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.1025 | 1.0 | 80 | 0.9880 | 0.6847 | 0.7201 | 0.6796 | 0.6821 | | 0.7366 | 2.0 | 160 | 0.6502 | 0.8098 | 0.8198 | 0.8103 | 0.8059 | | 0.6295 | 3.0 | 240 | 0.5303 | 0.8348 | 0.8410 | 0.8279 | 0.8270 | | 0.493 | 4.0 | 320 | 0.4666 | 0.8539 | 0.8522 | 0.8533 | 0.8491 | | 0.3939 | 5.0 | 400 | 0.4831 | 0.8579 | 0.8658 | 0.8503 | 0.8508 | | 0.3338 | 6.0 | 480 | 0.4551 | 0.8729 | 0.8711 | 0.8670 | 0.8665 | | 0.2841 | 7.0 | 560 | 0.4357 | 0.8939 | 0.8961 | 0.8931 | 0.8921 | | 0.2406 | 8.0 | 640 | 0.4074 | 0.8829 | 0.8820 | 0.8760 | 0.8776 | | 0.2008 | 9.0 | 720 | 0.4074 | 0.8909 | 0.8900 | 0.8872 | 0.8868 | | 0.2075 | 10.0 | 800 | 0.3910 | 0.8949 | 0.8942 | 0.8897 | 0.8904 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3