SmolLM2-360M-TemporalQuestions

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-360M on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0257
• F1: 0.9846

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: 0.001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 32
• total_train_batch_size: 1024
• total_eval_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine_with_restarts
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.086	1.0	223	0.0629	0.9514
0.1263	2.0	446	0.0466	0.9647
0.0172	3.0	669	0.0351	0.9745
0.0729	4.0	892	0.0319	0.9770
0.0254	5.0	1115	0.0320	0.9788
0.0258	6.0	1338	0.0288	0.9798
0.017	7.0	1561	0.0302	0.9812
0.0278	8.0	1784	0.0302	0.9807
0.0105	9.0	2007	0.0338	0.9797
0.0503	10.0	2230	0.0297	0.9808
0.0148	11.0	2453	0.0257	0.9846
0.0005	12.0	2676	0.0305	0.9822
0.0052	13.0	2899	0.0282	0.9853
0.0012	14.0	3122	0.0317	0.9837
0.0095	15.0	3345	0.0338	0.9859
0.0004	16.0	3568	0.0307	0.9865
0.0003	17.0	3791	0.0336	0.9856
0.0074	18.0	4014	0.0338	0.9855
0.0003	19.0	4237	0.0327	0.9864
0.0003	20.0	4460	0.0353	0.9858
0.0001	21.0	4683	0.0377	0.9858
0.0001	22.0	4906	0.0380	0.9870
0.0001	23.0	5129	0.0389	0.9866
0.0001	24.0	5352	0.0399	0.9866
0.0001	25.0	5575	0.0404	0.9866
0.0001	26.0	5798	0.0408	0.9867
0.0001	27.0	6021	0.0409	0.9867
0.0002	28.0	6244	0.0411	0.9867
0.0001	29.0	6467	0.0411	0.9867
0.0023	29.8691	6660	0.0412	0.9867

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu124
• Datasets 3.0.1
• Tokenizers 0.21.0