qa-persian-distilbert-fa-zwnj-base

This model is a fine-tuned version of makhataei/qa-persian-distilbert-fa-zwnj-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 5.3843

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: 6.25e-09
• train_batch_size: 14
• eval_batch_size: 14
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss
5.4975	1.0	9	5.3843
5.6974	2.0	18	5.3843
5.681	3.0	27	5.3843
5.7298	4.0	36	5.3843
5.7675	5.0	45	5.3843
5.7265	6.0	54	5.3843
5.6502	7.0	63	5.3843
5.6803	8.0	72	5.3843
5.6433	9.0	81	5.3843
5.6107	10.0	90	5.3843
5.5624	11.0	99	5.3843
5.6151	12.0	108	5.3843
5.6815	13.0	117	5.3843
5.6993	14.0	126	5.3843
5.6933	15.0	135	5.3843
5.7421	16.0	144	5.3843
5.7573	17.0	153	5.3843
5.7137	18.0	162	5.3843
5.7891	19.0	171	5.3843
5.7035	20.0	180	5.3843
5.6504	21.0	189	5.3843
5.7166	22.0	198	5.3843
5.6868	23.0	207	5.3843
5.7905	24.0	216	5.3843
5.7363	25.0	225	5.3843
5.7459	26.0	234	5.3843
5.7354	27.0	243	5.3843
5.7545	28.0	252	5.3843
5.6522	29.0	261	5.3843
5.6467	30.0	270	5.3843
5.7483	31.0	279	5.3843
5.7255	32.0	288	5.3843
5.6064	33.0	297	5.3843
5.6728	34.0	306	5.3843
5.6922	35.0	315	5.3843
5.6817	36.0	324	5.3843
5.6892	37.0	333	5.3843
5.609	38.0	342	5.3843
5.6179	39.0	351	5.3843
5.6384	40.0	360	5.3843
5.6311	41.0	369	5.3843
5.5614	42.0	378	5.3843
5.4875	43.0	387	5.3843
5.5113	44.0	396	5.3843
5.4597	45.0	405	5.3843
5.7105	46.0	414	5.3843
5.5722	47.0	423	5.3843
5.4466	48.0	432	5.3843
5.3902	49.0	441	5.3843
5.5197	50.0	450	5.3843
5.4349	51.0	459	5.3843
5.4746	52.0	468	5.3843
5.5058	53.0	477	5.3843
5.5615	54.0	486	5.3843
5.5838	55.0	495	5.3843
5.6564	56.0	504	5.3843
5.6402	57.0	513	5.3843
5.6022	58.0	522	5.3843
5.6428	59.0	531	5.3843
5.6259	60.0	540	5.3843
5.6678	61.0	549	5.3843
5.6119	62.0	558	5.3843
5.614	63.0	567	5.3843
5.6349	64.0	576	5.3843
5.5935	65.0	585	5.3843
5.7087	66.0	594	5.3843
5.6243	67.0	603	5.3843
5.6718	68.0	612	5.3843
5.5945	69.0	621	5.3843
5.6609	70.0	630	5.3843
5.7069	71.0	639	5.3843
5.6578	72.0	648	5.3843
5.706	73.0	657	5.3843
5.7486	74.0	666	5.3843
5.5958	75.0	675	5.3843
5.6005	76.0	684	5.3843
5.6954	77.0	693	5.3843
5.6576	78.0	702	5.3843
5.6537	79.0	711	5.3843
5.6949	80.0	720	5.3843
5.7134	81.0	729	5.3843
5.7391	82.0	738	5.3843
5.5262	83.0	747	5.3843
5.7075	84.0	756	5.3843
5.6827	85.0	765	5.3843
5.6573	86.0	774	5.3843
5.738	87.0	783	5.3843
5.7347	88.0	792	5.3843
5.6938	89.0	801	5.3843
5.7081	90.0	810	5.3843
5.7208	91.0	819	5.3843
5.7367	92.0	828	5.3843
5.7761	93.0	837	5.3843
5.7187	94.0	846	5.3843
5.7559	95.0	855	5.3843
5.7001	96.0	864	5.3843
5.7402	97.0	873	5.3843
5.6641	98.0	882	5.3843
5.7209	99.0	891	5.3843
5.7791	100.0	900	5.3843

Framework versions

• Transformers 4.35.2
• Pytorch 2.0.1+cu117
• Datasets 2.15.0
• Tokenizers 0.15.0