exceptions_exp2_resemble_to_push_frequency_1032

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

• Loss: 3.5762
• Accuracy: 0.3671

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: 0.0006
• train_batch_size: 16
• eval_batch_size: 16
• seed: 1032
• gradient_accumulation_steps: 5
• total_train_batch_size: 80
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.8077	0.2912	1000	4.7368	0.2569
4.3355	0.5825	2000	4.2787	0.3002
4.1543	0.8737	3000	4.0961	0.3152
3.9969	1.1648	4000	3.9885	0.3255
3.9235	1.4561	5000	3.9142	0.3318
3.8897	1.7473	6000	3.8568	0.3369
3.7427	2.0384	7000	3.8139	0.3415
3.7463	2.3297	8000	3.7830	0.3447
3.7415	2.6209	9000	3.7528	0.3472
3.7289	2.9122	10000	3.7289	0.3495
3.6325	3.2033	11000	3.7154	0.3517
3.6268	3.4945	12000	3.6962	0.3535
3.6435	3.7858	13000	3.6783	0.3549
3.5409	4.0769	14000	3.6700	0.3560
3.5812	4.3681	15000	3.6613	0.3573
3.5711	4.6594	16000	3.6464	0.3587
3.5748	4.9506	17000	3.6347	0.3596
3.5007	5.2417	18000	3.6364	0.3600
3.5337	5.5330	19000	3.6263	0.3612
3.5421	5.8242	20000	3.6151	0.3621
3.4317	6.1153	21000	3.6184	0.3624
3.4691	6.4066	22000	3.6099	0.3631
3.4836	6.6978	23000	3.5993	0.3637
3.4874	6.9890	24000	3.5905	0.3647
3.4321	7.2802	25000	3.5986	0.3646
3.4369	7.5714	26000	3.5912	0.3653
3.4537	7.8627	27000	3.5794	0.3662
3.3668	8.1538	28000	3.5914	0.3661
3.4093	8.4450	29000	3.5846	0.3664
3.431	8.7363	30000	3.5762	0.3671
3.3184	9.0274	31000	3.5798	0.3674
3.377	9.3186	32000	3.5809	0.3672
3.3939	9.6099	33000	3.5724	0.3678
3.4109	9.9011	34000	3.5611	0.3685
3.3373	10.1922	35000	3.5772	0.3682
3.3609	10.4835	36000	3.5684	0.3685
3.4038	10.7747	37000	3.5599	0.3691
3.2944	11.0658	38000	3.5725	0.3690
3.3442	11.3571	39000	3.5660	0.3691
3.3584	11.6483	40000	3.5603	0.3698
3.3727	11.9395	41000	3.5518	0.3700
3.2891	12.2307	42000	3.5657	0.3697
3.3365	12.5219	43000	3.5583	0.3700
3.3517	12.8131	44000	3.5518	0.3705
3.2635	13.1043	45000	3.5615	0.3704
3.3246	13.3955	46000	3.5549	0.3705
3.326	13.6867	47000	3.5490	0.3709
3.3341	13.9780	48000	3.5432	0.3714
3.2913	14.2691	49000	3.5589	0.3709
3.2971	14.5603	50000	3.5523	0.3709
3.3271	14.8516	51000	3.5445	0.3714
3.2435	15.1427	52000	3.5589	0.3713
3.281	15.4339	53000	3.5551	0.3716
3.3023	15.7252	54000	3.5442	0.3719
3.2045	16.0163	55000	3.5526	0.3717
3.2608	16.3075	56000	3.5529	0.3716
3.2856	16.5988	57000	3.5463	0.3720
3.2891	16.8900	58000	3.5398	0.3726
3.2247	17.1812	59000	3.5556	0.3718
3.2549	17.4724	60000	3.5466	0.3721
3.2807	17.7636	61000	3.5407	0.3727
3.1888	18.0548	62000	3.5530	0.3723
3.2371	18.3460	63000	3.5507	0.3722
3.2615	18.6372	64000	3.5429	0.3731
3.2662	18.9285	65000	3.5351	0.3733
3.2062	19.2196	66000	3.5497	0.3728
3.242	19.5108	67000	3.5484	0.3726
3.2734	19.8021	68000	3.5384	0.3733
3.1755	20.0932	69000	3.5544	0.3727
3.2174	20.3844	70000	3.5512	0.3728
3.2381	20.6757	71000	3.5423	0.3736
3.2437	20.9669	72000	3.5343	0.3738
3.204	21.2580	73000	3.5536	0.3726
3.2134	21.5493	74000	3.5462	0.3731
3.2241	21.8405	75000	3.5392	0.3737
3.1705	22.1316	76000	3.5515	0.3733
3.2019	22.4229	77000	3.5471	0.3737
3.2065	22.7141	78000	3.5403	0.3739
3.194	23.0052	79000	3.5451	0.3738
3.1815	23.2965	80000	3.5515	0.3735
3.218	23.5877	81000	3.5433	0.3741
3.2422	23.8790	82000	3.5338	0.3743
3.1459	24.1701	83000	3.5529	0.3735
3.1993	24.4613	84000	3.5506	0.3736
3.2121	24.7526	85000	3.5380	0.3745
3.1188	25.0437	86000	3.5527	0.3739
3.1541	25.3349	87000	3.5510	0.3739
3.182	25.6262	88000	3.5436	0.3743
3.2141	25.9174	89000	3.5343	0.3747
3.1395	26.2085	90000	3.5566	0.3738
3.1696	26.4998	91000	3.5447	0.3743
3.1961	26.7910	92000	3.5415	0.3744
3.1084	27.0821	93000	3.5532	0.3741
3.1561	27.3734	94000	3.5487	0.3745
3.1751	27.6646	95000	3.5432	0.3745
3.1853	27.9558	96000	3.5360	0.3749
3.139	28.2470	97000	3.5545	0.3739
3.1499	28.5382	98000	3.5472	0.3746
3.1747	28.8295	99000	3.5374	0.3749
3.1073	29.1206	100000	3.5580	0.3740
3.1268	29.4118	101000	3.5532	0.3743
3.1554	29.7031	102000	3.5421	0.3749

Framework versions

• Transformers 4.55.2
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.21.4