CodeIsAbstract/HybridModelScratch_lightn_Conti

1---2library_name: transformers3tags:4- generated_from_trainer5metrics:6- accuracy7model-index:8- name: HybridModelScratch_lightn_Conti9  results: []10---11 12<!-- This model card has been generated automatically according to the information the Trainer had access to. You13should probably proofread and complete it, then remove this comment. -->14 15# HybridModelScratch_lightn_Conti16 17This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.18It achieves the following results on the evaluation set:19- Loss: 3.274920- Accuracy: 0.384821 22## Model description23 24More information needed25 26## Intended uses & limitations27 28More information needed29 30## Training and evaluation data31 32More information needed33 34## Training procedure35 36### Training hyperparameters37 38The following hyperparameters were used during training:39- learning_rate: 0.00140- train_batch_size: 12041- eval_batch_size: 1642- seed: 4243- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments44- lr_scheduler_type: cosine45- lr_scheduler_warmup_steps: 15046- training_steps: 5000047 48### Training results49 50| Training Loss | Epoch | Step  | Accuracy | Validation Loss |51|:-------------:|:-----:|:-----:|:--------:|:---------------:|52| 4.5799        | 0.02  | 1000  | 0.2697   | 4.5051          |53| 4.1703        | 0.04  | 2000  | 0.3056   | 4.1036          |54| 4.0044        | 0.06  | 3000  | 0.3195   | 3.9451          |55| 3.8638        | 0.08  | 4000  | 0.3281   | 3.8499          |56| 3.7991        | 0.1   | 5000  | 0.3350   | 3.7702          |57| 3.6996        | 0.12  | 6000  | 0.3414   | 3.7112          |58| 3.6319        | 0.14  | 7000  | 0.3448   | 3.6666          |59| 3.6062        | 0.16  | 8000  | 0.3476   | 3.6405          |60| 3.6643        | 0.18  | 9000  | 0.3498   | 3.6175          |61| 3.5992        | 0.2   | 10000 | 0.3535   | 3.5797          |62| 3.5671        | 0.22  | 11000 | 0.3559   | 3.5543          |63| 3.5230        | 0.24  | 12000 | 0.3578   | 3.5308          |64| 3.5930        | 0.26  | 13000 | 0.3593   | 3.5190          |65| 3.5475        | 0.28  | 14000 | 0.3616   | 3.4961          |66| 3.5220        | 0.3   | 15000 | 0.3633   | 3.4778          |67| 3.5050        | 0.32  | 16000 | 0.3644   | 3.4654          |68| 3.4776        | 0.34  | 17000 | 0.3666   | 3.4458          |69| 3.4829        | 0.36  | 18000 | 0.3674   | 3.4346          |70| 3.4483        | 0.38  | 19000 | 0.3694   | 3.4189          |71| 3.4418        | 0.4   | 20000 | 0.3704   | 3.4063          |72| 3.4183        | 0.42  | 21000 | 0.3720   | 3.3956          |73| 3.3858        | 0.44  | 22000 | 0.3725   | 3.3870          |74| 3.3654        | 0.46  | 23000 | 0.3728   | 3.3824          |75| 3.3943        | 0.48  | 24000 | 0.3734   | 3.3756          |76| 3.3957        | 0.5   | 25000 | 0.3757   | 3.3548          |77| 3.3787        | 0.52  | 26000 | 0.3769   | 3.3452          |78| 3.3605        | 0.54  | 27000 | 0.3777   | 3.3393          |79| 3.3707        | 0.56  | 28000 | 0.3787   | 3.3302          |80| 3.3508        | 0.58  | 29000 | 0.3793   | 3.3221          |81| 3.3566        | 0.6   | 30000 | 0.3802   | 3.3148          |82| 3.3441        | 0.62  | 31000 | 0.3809   | 3.3054          |83| 3.3268        | 0.64  | 32000 | 0.3814   | 3.3028          |84| 3.0620        | 0.02  | 33000 | 3.3109   | 0.3804          |85| 3.0159        | 0.04  | 34000 | 3.3074   | 0.3810          |86| 3.0230        | 0.06  | 35000 | 3.3025   | 0.3815          |87| 3.0335        | 0.08  | 36000 | 3.2988   | 0.3821          |88| 3.0098        | 0.1   | 37000 | 3.2959   | 0.3826          |89| 3.0096        | 0.12  | 38000 | 3.2932   | 0.3826          |90| 2.9760        | 0.14  | 39000 | 3.2904   | 0.3831          |91| 2.9701        | 0.16  | 40000 | 3.2869   | 0.3832          |92| 2.9654        | 0.18  | 41000 | 3.2847   | 0.3837          |93| 2.9752        | 0.2   | 42000 | 3.2823   | 0.3839          |94| 2.9480        | 0.22  | 43000 | 3.2810   | 0.3840          |95| 2.9637        | 0.24  | 44000 | 3.2790   | 0.3842          |96| 2.9721        | 0.26  | 45000 | 3.2772   | 0.3843          |97| 2.9694        | 0.28  | 46000 | 3.2765   | 0.3847          |98| 2.9577        | 0.3   | 47000 | 3.2756   | 0.3847          |99| 2.9573        | 0.32  | 48000 | 3.2751   | 0.3847          |100| 2.9537        | 0.34  | 49000 | 3.2749   | 0.3848          |101| 2.9677        | 0.36  | 50000 | 3.2749   | 0.3848          |102 103 104### Framework versions105 106- Transformers 5.13.0107- Pytorch 2.8.0+cu128108- Datasets 5.0.0109- Tokenizers 0.22.2110