library_name, base_model, tags, model-index

library_name	base_model	tags	model-index
transformers	CausalNLP/gpt2-hf_multilingual-20	generated_from_trainer	name results gpt2-multilingual-20-arabic-repair_3epochs_lr1e-4_bf16

gpt2-multilingual-20-arabic-repair_3epochs_lr1e-4_bf16

This model is a fine-tuned version of CausalNLP/gpt2-hf_multilingual-20 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.2206

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: 0.0001
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Use adamw_torch_fused with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.295	0.0799	500	3.3488
3.2752	0.1597	1000	3.3363
3.2844	0.2396	1500	3.3416
3.3059	0.3194	2000	3.3491
3.335	0.3993	2500	3.3387
3.2845	0.4791	3000	3.3301
3.2458	0.5590	3500	3.3216
3.2689	0.6388	4000	3.3129
3.2737	0.7187	4500	3.3069
3.2604	0.7985	5000	3.3000
3.2255	0.8784	5500	3.2936
3.239	0.9582	6000	3.2882
3.2594	1.0380	6500	3.2832
3.2123	1.1179	7000	3.2780
3.2347	1.1977	7500	3.2726
3.2197	1.2776	8000	3.2671
3.2028	1.3574	8500	3.2615
3.195	1.4373	9000	3.2557
3.2049	1.5171	9500	3.2510
3.1649	1.5970	10000	3.2458
3.1984	1.6768	10500	3.2409
3.2787	1.7567	11000	3.2374
3.1966	1.8365	11500	3.2334
3.166	1.9164	12000	3.2305
3.2029	1.9962	12500	3.2272
3.1629	2.0760	13000	3.2268
3.1115	2.1559	13500	3.2253
3.184	2.2357	14000	3.2239
3.1391	2.3156	14500	3.2229
3.1341	2.3954	15000	3.2220
3.1623	2.4753	15500	3.2214
3.1565	2.5551	16000	3.2211
3.1129	2.6350	16500	3.2208
3.2043	2.7148	17000	3.2207
3.1044	2.7947	17500	3.2206
3.1922	2.8746	18000	3.2206
3.1229	2.9544	18500	3.2206

Framework versions

• Transformers 4.57.4
• Pytorch 2.9.1+cu128
• Datasets 4.4.2
• Tokenizers 0.22.2