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Yanmife/
nllb-menyo

Name: nllb-menyo
Availability: InStock

facebook/nllb-200-distilled-600M

cc-by-nc-4.0

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nllb-menyo

This model is a fine-tuned version of facebook/nllb-200-distilled-600M on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0240

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
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
lr_scheduler_warmup_steps: 1000
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.7656	0.7663	200	4.0671
3.4231	1.5326	400	2.8752
2.4958	2.2989	600	2.3280
2.2866	3.0651	800	2.2600
2.2179	3.8314	1000	2.2216
2.1699	4.5977	1200	2.1922
2.1431	5.3640	1400	2.1698
2.1148	6.1303	1600	2.1553
2.0856	6.8966	1800	2.1395
2.0729	7.6628	2000	2.1275
2.0532	8.4291	2200	2.1258
2.039	9.1954	2400	2.1135
2.025	9.9617	2600	2.1131
2.0123	10.7280	2800	2.1075
1.9932	11.4943	3000	2.1025
2.0002	12.2605	3200	2.0961
1.9817	13.0268	3400	2.0899
1.9688	13.7931	3600	2.0858
1.9606	14.5594	3800	2.0827
1.9531	15.3257	4000	2.0806
1.9392	16.0920	4200	2.0748
1.9354	16.8582	4400	2.0723
1.9283	17.6245	4600	2.0679
1.9166	18.3908	4800	2.0671
1.9065	19.1571	5000	2.0594
1.9092	19.9234	5200	2.0576
1.9041	20.6897	5400	2.0548
1.8753	21.4559	5600	2.0578
1.8914	22.2222	5800	2.0515
1.8742	22.9885	6000	2.0486
1.8716	23.7548	6200	2.0496
1.8775	24.5211	6400	2.0453
1.86	25.2874	6600	2.0424
1.8531	26.0536	6800	2.0420
1.8522	26.8199	7000	2.0397
1.8536	27.5862	7200	2.0388
1.8497	28.3525	7400	2.0364
1.8442	29.1188	7600	2.0353
1.8387	29.8851	7800	2.0337
1.8413	30.6513	8000	2.0330
1.8217	31.4176	8200	2.0358
1.8356	32.1839	8400	2.0306
1.8243	32.9502	8600	2.0289
1.8242	33.7165	8800	2.0294
1.8197	34.4828	9000	2.0276
1.8116	35.2490	9200	2.0281
1.8229	36.0153	9400	2.0274
1.8135	36.7816	9600	2.0271
1.8135	37.5479	9800	2.0270
1.8113	38.3142	10000	2.0264
1.8165	39.0805	10200	2.0253
1.8133	39.8467	10400	2.0244
1.8082	40.6130	10600	2.0236
1.8048	41.3793	10800	2.0230
1.8077	42.1456	11000	2.0257
1.8022	42.9119	11200	2.0237
1.8005	43.6782	11400	2.0244
1.806	44.4444	11600	2.0236
1.8028	45.2107	11800	2.0243
1.8053	45.9770	12000	2.0237
1.8074	46.7433	12200	2.0235
1.8009	47.5096	12400	2.0240
1.7992	48.2759	12600	2.0240
1.8069	49.0421	12800	2.0240

Framework versions

PEFT 0.17.0
Transformers 4.55.2
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2

Yanmife/nllb-menyo

nllb-menyo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Yanmife/
nllb-menyo