** DATASET for train

1-passage score does not have to be 0,1. it can be a range from 0 to 1 (0,0.25,0.5,0.75,1) : we can get this core by llm and apply it in loss calculation.

2-dataset needs preprocesing of removing negetive or positive passage by llm.

3-miracle dataset: question = 2107   - passages = 21844 : some negetive passage can be related

4-cross ligual dataset can be useful : query = first language  - passage = second language

5-swim-ir dataset : they have passage and they have created query from it : it is shit for persian

6-parsinlu dataset: question = 600 - passage = 600 : all are positive

7-persianqa dataset: question = 6306 - passage = less than queries : every passage has multiple queries : all are positive - be careful some query is impossible to anser

8-pquad dataset  :question = 48273 - passage = 10082 : every passage has multiple queries :  be careful some query is impossible to anser : all are positive

9-longragfa dataset: it is long doc and query and for evaluation : question = 250, passage = 1500 : not using

10-Synthetic-persian-qa-retrieval dataset : question = 223423, passage = 250000 : negetaive passage are not exactly different : needs preprocessing

no train
NDCG: 0.8452119768348717
Recall 7: 0.3373666606161222
Recall 12: 0.48390155482482855
Recall 20: 0.6340810809380268
Recall Variant: 0.44313617731261423
Precision 7: 0.4714285714285715
Precision 12: 0.41999999999999993
Precision 20: 0.358

train with 100
NDCG: 0.8007791818263832
Recall 7: 0.2617863643550479
Recall 12: 0.3759745806720163
Recall 20: 0.5564983103150418
Recall Variant: 0.36642345327979325
Precision 7: 0.3828571428571429
Precision 12: 0.3449999999999999
Precision 20: 0.311

train with 100 with lora 
NDCG: 0.8432282495018343
Recall 7: 0.33695911259587386
Recall 12: 0.4729916144600827
Recall 20: 0.6212526155736547
Recall Variant: 0.43208929205133273
Precision 7: 0.4685714285714285
Precision 12: 0.4099999999999999
Precision 20: 0.35200000000000004

train with 100 with promt