m2m100_418M-finetuned-hi-to-en

This model is a fine-tuned version of facebook/m2m100_418M on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1973
• Bleu: 0.0
• Gen Len: 5.7184

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
2.6398	0.1100	500	2.5624	2.434	5.8204
2.6877	0.2199	1000	2.4067	6.9764	5.6658
2.6	0.3299	1500	2.3000	4.9574	5.6818
2.5495	0.4399	2000	2.2093	13.5783	5.7773
2.4986	0.5498	2500	2.1232	12.0884	5.7156
2.4475	0.6598	3000	2.0526	0.0	5.7829
2.418	0.7697	3500	1.9804	0.0	5.7902
2.3652	0.8797	4000	1.9253	0.0	5.7564
2.3625	0.9897	4500	1.8681	0.0	5.7984
2.024	1.0996	5000	1.8020	0.0	5.81
2.0017	1.2096	5500	1.7601	0.0	5.7493
2.0036	1.3196	6000	1.7208	0.0	5.8507
1.9983	1.4295	6500	1.6662	0.0	5.742
1.9838	1.5395	7000	1.6273	0.0	5.8033
1.9755	1.6494	7500	1.5914	0.0	5.8629
1.9679	1.7594	8000	1.5436	0.0	5.8751
1.9386	1.8694	8500	1.5154	0.0	5.8762
1.9299	1.9793	9000	1.4725	0.0	5.82
1.6886	2.0893	9500	1.4242	0.0	5.7729
1.6454	2.1993	10000	1.3867	0.0	5.7042
1.6361	2.3092	10500	1.3544	0.0	5.6789
1.6482	2.4192	11000	1.3346	0.0	5.7051
1.6528	2.5291	11500	1.3043	0.0	5.7147
1.6687	2.6391	12000	1.2718	0.0	5.7633
1.6428	2.7491	12500	1.2417	0.0	5.7318
1.6547	2.8590	13000	1.2086	0.0	5.7536
1.6467	2.9690	13500	1.1895	0.0	5.7458
1.4526	3.0790	14000	1.1425	0.0	5.7869
1.3555	3.1889	14500	1.1204	0.0	5.7491
1.4007	3.2989	15000	1.1010	0.0	5.8267
1.3799	3.4088	15500	1.0754	0.0	5.7482
1.401	3.5188	16000	1.0460	0.0	5.7571
1.4093	3.6288	16500	1.0239	0.0	5.7262
1.3997	3.7387	17000	1.0024	0.0	5.692
1.4162	3.8487	17500	0.9869	0.0	5.7273
1.4102	3.9587	18000	0.9558	0.0	5.7613
1.2476	4.0686	18500	0.9296	0.0	5.7113
1.1591	4.1786	19000	0.9163	0.0	5.7651
1.1861	4.2885	19500	0.9017	0.0	5.7498
1.1799	4.3985	20000	0.8841	0.0	5.7884
1.1902	4.5085	20500	0.8635	0.0	5.7613
1.193	4.6184	21000	0.8448	0.0	5.7507
1.1955	4.7284	21500	0.8266	0.0	5.7602
1.2062	4.8384	22000	0.8069	0.0	5.7562
1.2058	4.9483	22500	0.7805	0.0	5.7087
1.0832	5.0583	23000	0.7583	0.0	5.7631
0.9869	5.1682	23500	0.7497	0.0	5.7284
0.9956	5.2782	24000	0.7356	0.0	5.7438
1.0164	5.3882	24500	0.7253	0.0	5.7789
1.017	5.4981	25000	0.7075	0.0	5.7462
1.0365	5.6081	25500	0.6890	0.0	5.7487
1.0421	5.7181	26000	0.6770	0.0	5.7547
1.0344	5.8280	26500	0.6560	0.0	5.7624
1.0286	5.9380	27000	0.6429	0.0	5.7816
0.9637	6.0479	27500	0.6257	0.0	5.7547
0.8297	6.1579	28000	0.6144	0.0	5.7649
0.8625	6.2679	28500	0.6038	0.0	5.7442
0.8587	6.3778	29000	0.5889	0.0	5.7633
0.8732	6.4878	29500	0.5788	0.0	5.7676
0.8738	6.5978	30000	0.5673	0.0	5.7698
0.8938	6.7077	30500	0.5521	0.0	5.7929
0.8797	6.8177	31000	0.5410	0.0	5.7542
0.9055	6.9276	31500	0.5284	0.0	5.7551
0.8408	7.0376	32000	0.5154	0.0	5.754
0.7278	7.1476	32500	0.5106	0.0	5.7602
0.7357	7.2575	33000	0.4958	0.0	5.7422
0.7498	7.3675	33500	0.4906	0.0	5.734
0.7524	7.4775	34000	0.4804	0.0	5.7136
0.7609	7.5874	34500	0.4716	0.0	5.7504
0.7555	7.6974	35000	0.4621	38.6861	5.7544
0.7752	7.8073	35500	0.4493	0.0	5.7429
0.7656	7.9173	36000	0.4387	0.0	5.7484
0.7329	8.0273	36500	0.4281	0.0	5.7364
0.6314	8.1372	37000	0.4251	0.0	5.7453
0.6595	8.2472	37500	0.4161	0.0	5.7393
0.6566	8.3572	38000	0.4125	0.0	5.7502
0.6582	8.4671	38500	0.4043	0.0	5.7364
0.6579	8.5771	39000	0.3962	0.0	5.7422
0.6622	8.6870	39500	0.3878	0.0	5.76
0.6547	8.7970	40000	0.3790	0.0	5.7642
0.6682	8.9070	40500	0.3701	0.0	5.7549
0.6499	9.0169	41000	0.3584	0.0	5.7333
0.541	9.1269	41500	0.3547	0.0	5.7398
0.5621	9.2369	42000	0.3519	0.0	5.7322
0.5673	9.3468	42500	0.3458	0.0	5.7467
0.5618	9.4568	43000	0.3407	0.0	5.7382
0.5704	9.5667	43500	0.3326	0.0	5.7536
0.5816	9.6767	44000	0.3292	0.0	5.7349
0.5892	9.7867	44500	0.3194	0.0	5.7358
0.5796	9.8966	45000	0.3129	0.0	5.7369
0.5807	10.0066	45500	0.3079	0.0	5.7404
0.4786	10.1166	46000	0.3033	0.0	5.7491
0.4863	10.2265	46500	0.2989	0.0	5.7331
0.4979	10.3365	47000	0.2968	0.0	5.732
0.5015	10.4464	47500	0.2917	0.0	5.7229
0.5105	10.5564	48000	0.2886	0.0	5.7398
0.5039	10.6664	48500	0.2830	0.0	5.7173
0.5202	10.7763	49000	0.2789	0.0	5.7218
0.5123	10.8863	49500	0.2742	0.0	5.7276
0.5043	10.9963	50000	0.2670	0.0	5.7191
0.4314	11.1062	50500	0.2661	0.0	5.7364
0.4345	11.2162	51000	0.2612	0.0	5.7262
0.4411	11.3261	51500	0.2592	0.0	5.7233
0.447	11.4361	52000	0.2568	0.0	5.7344
0.453	11.5461	52500	0.2528	0.0	5.7231
0.4485	11.6560	53000	0.2496	0.0	5.7311
0.4472	11.7660	53500	0.2460	0.0	5.7167
0.4567	11.8760	54000	0.2412	0.0	5.7256
0.4528	11.9859	54500	0.2381	0.0	5.7264
0.404	12.0959	55000	0.2342	0.0	5.7187
0.3995	12.2059	55500	0.2333	0.0	5.7293
0.3989	12.3158	56000	0.2317	0.0	5.7104
0.3988	12.4258	56500	0.2284	0.0	5.7242
0.3991	12.5357	57000	0.2261	0.0	5.7276
0.4075	12.6457	57500	0.2234	0.0	5.7198
0.4074	12.7557	58000	0.2207	0.0	5.7262
0.398	12.8656	58500	0.2178	0.0	5.7282
0.4003	12.9756	59000	0.2162	0.0	5.7291
0.374	13.0856	59500	0.2145	0.0	5.7271
0.3749	13.1955	60000	0.2126	0.0	5.7287
0.3589	13.3055	60500	0.2109	0.0	5.7356
0.3734	13.4154	61000	0.2095	0.0	5.7329
0.3706	13.5254	61500	0.2087	0.0	5.7327
0.3781	13.6354	62000	0.2071	0.0	5.7296
0.3735	13.7453	62500	0.2060	0.0	5.7287
0.372	13.8553	63000	0.2039	0.0	5.718
0.3751	13.9653	63500	0.2024	0.0	5.728
0.3573	14.0752	64000	0.2014	0.0	5.7189
0.3322	14.1852	64500	0.2010	0.0	5.7204
0.3359	14.2951	65000	0.2003	0.0	5.7227
0.3533	14.4051	65500	0.1994	0.0	5.7222
0.3489	14.5151	66000	0.1986	0.0	5.7198
0.3358	14.6250	66500	0.1981	0.0	5.7231
0.3424	14.7350	67000	0.1977	0.0	5.72
0.3341	14.8450	67500	0.1976	0.0	5.7209
0.3513	14.9549	68000	0.1973	0.0	5.7184

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1