
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# islamic_qa

This model is a fine-tuned version of [Huzaifa30/islamic_qa](https://huggingface.co/Huzaifa30/islamic_qa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6492

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 11   | 0.8716          |
| No log        | 2.0   | 22   | 0.8629          |
| No log        | 3.0   | 33   | 0.8406          |
| No log        | 4.0   | 44   | 0.8342          |
| No log        | 5.0   | 55   | 0.8217          |
| No log        | 6.0   | 66   | 0.8047          |
| No log        | 7.0   | 77   | 0.8063          |
| No log        | 8.0   | 88   | 0.7846          |
| No log        | 9.0   | 99   | 0.7831          |
| No log        | 10.0  | 110  | 0.7685          |
| No log        | 11.0  | 121  | 0.7595          |
| No log        | 12.0  | 132  | 0.7517          |
| No log        | 13.0  | 143  | 0.7529          |
| No log        | 14.0  | 154  | 0.7423          |
| No log        | 15.0  | 165  | 0.7344          |
| No log        | 16.0  | 176  | 0.7237          |
| No log        | 17.0  | 187  | 0.7227          |
| No log        | 18.0  | 198  | 0.7251          |
| No log        | 19.0  | 209  | 0.7246          |
| No log        | 20.0  | 220  | 0.7082          |
| No log        | 21.0  | 231  | 0.7089          |
| No log        | 22.0  | 242  | 0.7010          |
| No log        | 23.0  | 253  | 0.6895          |
| No log        | 24.0  | 264  | 0.6912          |
| No log        | 25.0  | 275  | 0.6979          |
| No log        | 26.0  | 286  | 0.6846          |
| No log        | 27.0  | 297  | 0.6810          |
| No log        | 28.0  | 308  | 0.6859          |
| No log        | 29.0  | 319  | 0.6761          |
| No log        | 30.0  | 330  | 0.6775          |
| No log        | 31.0  | 341  | 0.6696          |
| No log        | 32.0  | 352  | 0.6762          |
| No log        | 33.0  | 363  | 0.6744          |
| No log        | 34.0  | 374  | 0.6746          |
| No log        | 35.0  | 385  | 0.6771          |
| No log        | 36.0  | 396  | 0.6701          |
| No log        | 37.0  | 407  | 0.6696          |
| No log        | 38.0  | 418  | 0.6654          |
| No log        | 39.0  | 429  | 0.6702          |
| No log        | 40.0  | 440  | 0.6668          |
| No log        | 41.0  | 451  | 0.6529          |
| No log        | 42.0  | 462  | 0.6717          |
| No log        | 43.0  | 473  | 0.6556          |
| No log        | 44.0  | 484  | 0.6567          |
| No log        | 45.0  | 495  | 0.6649          |
| 0.4121        | 46.0  | 506  | 0.6584          |
| 0.4121        | 47.0  | 517  | 0.6611          |
| 0.4121        | 48.0  | 528  | 0.6642          |
| 0.4121        | 49.0  | 539  | 0.6542          |
| 0.4121        | 50.0  | 550  | 0.6446          |
| 0.4121        | 51.0  | 561  | 0.6651          |
| 0.4121        | 52.0  | 572  | 0.6551          |
| 0.4121        | 53.0  | 583  | 0.6597          |
| 0.4121        | 54.0  | 594  | 0.6528          |
| 0.4121        | 55.0  | 605  | 0.6578          |
| 0.4121        | 56.0  | 616  | 0.6662          |
| 0.4121        | 57.0  | 627  | 0.6525          |
| 0.4121        | 58.0  | 638  | 0.6537          |
| 0.4121        | 59.0  | 649  | 0.6553          |
| 0.4121        | 60.0  | 660  | 0.6510          |
| 0.4121        | 61.0  | 671  | 0.6544          |
| 0.4121        | 62.0  | 682  | 0.6411          |
| 0.4121        | 63.0  | 693  | 0.6448          |
| 0.4121        | 64.0  | 704  | 0.6430          |
| 0.4121        | 65.0  | 715  | 0.6468          |
| 0.4121        | 66.0  | 726  | 0.6345          |
| 0.4121        | 67.0  | 737  | 0.6615          |
| 0.4121        | 68.0  | 748  | 0.6547          |
| 0.4121        | 69.0  | 759  | 0.6509          |
| 0.4121        | 70.0  | 770  | 0.6446          |
| 0.4121        | 71.0  | 781  | 0.6456          |
| 0.4121        | 72.0  | 792  | 0.6389          |
| 0.4121        | 73.0  | 803  | 0.6486          |
| 0.4121        | 74.0  | 814  | 0.6497          |
| 0.4121        | 75.0  | 825  | 0.6506          |
| 0.4121        | 76.0  | 836  | 0.6510          |
| 0.4121        | 77.0  | 847  | 0.6460          |
| 0.4121        | 78.0  | 858  | 0.6423          |
| 0.4121        | 79.0  | 869  | 0.6457          |
| 0.4121        | 80.0  | 880  | 0.6559          |
| 0.4121        | 81.0  | 891  | 0.6558          |
| 0.4121        | 82.0  | 902  | 0.6422          |
| 0.4121        | 83.0  | 913  | 0.6525          |
| 0.4121        | 84.0  | 924  | 0.6528          |
| 0.4121        | 85.0  | 935  | 0.6499          |
| 0.4121        | 86.0  | 946  | 0.6633          |
| 0.4121        | 87.0  | 957  | 0.6512          |
| 0.4121        | 88.0  | 968  | 0.6543          |
| 0.4121        | 89.0  | 979  | 0.6541          |
| 0.4121        | 90.0  | 990  | 0.6480          |
| 0.1789        | 91.0  | 1001 | 0.6502          |
| 0.1789        | 92.0  | 1012 | 0.6447          |
| 0.1789        | 93.0  | 1023 | 0.6455          |
| 0.1789        | 94.0  | 1034 | 0.6530          |
| 0.1789        | 95.0  | 1045 | 0.6444          |
| 0.1789        | 96.0  | 1056 | 0.6457          |
| 0.1789        | 97.0  | 1067 | 0.6451          |
| 0.1789        | 98.0  | 1078 | 0.6507          |
| 0.1789        | 99.0  | 1089 | 0.6602          |
| 0.1789        | 100.0 | 1100 | 0.6560          |
| 0.1789        | 101.0 | 1111 | 0.6503          |
| 0.1789        | 102.0 | 1122 | 0.6408          |
| 0.1789        | 103.0 | 1133 | 0.6430          |
| 0.1789        | 104.0 | 1144 | 0.6476          |
| 0.1789        | 105.0 | 1155 | 0.6485          |
| 0.1789        | 106.0 | 1166 | 0.6489          |
| 0.1789        | 107.0 | 1177 | 0.6564          |
| 0.1789        | 108.0 | 1188 | 0.6537          |
| 0.1789        | 109.0 | 1199 | 0.6536          |
| 0.1789        | 110.0 | 1210 | 0.6539          |
| 0.1789        | 111.0 | 1221 | 0.6564          |
| 0.1789        | 112.0 | 1232 | 0.6531          |
| 0.1789        | 113.0 | 1243 | 0.6491          |
| 0.1789        | 114.0 | 1254 | 0.6519          |
| 0.1789        | 115.0 | 1265 | 0.6431          |
| 0.1789        | 116.0 | 1276 | 0.6437          |
| 0.1789        | 117.0 | 1287 | 0.6471          |
| 0.1789        | 118.0 | 1298 | 0.6370          |
| 0.1789        | 119.0 | 1309 | 0.6385          |
| 0.1789        | 120.0 | 1320 | 0.6473          |
| 0.1789        | 121.0 | 1331 | 0.6480          |
| 0.1789        | 122.0 | 1342 | 0.6427          |
| 0.1789        | 123.0 | 1353 | 0.6464          |
| 0.1789        | 124.0 | 1364 | 0.6486          |
| 0.1789        | 125.0 | 1375 | 0.6395          |
| 0.1789        | 126.0 | 1386 | 0.6452          |
| 0.1789        | 127.0 | 1397 | 0.6492          |
| 0.1789        | 128.0 | 1408 | 0.6501          |
| 0.1789        | 129.0 | 1419 | 0.6418          |
| 0.1789        | 130.0 | 1430 | 0.6353          |
| 0.1789        | 131.0 | 1441 | 0.6411          |
| 0.1789        | 132.0 | 1452 | 0.6477          |
| 0.1789        | 133.0 | 1463 | 0.6453          |
| 0.1789        | 134.0 | 1474 | 0.6445          |
| 0.1789        | 135.0 | 1485 | 0.6507          |
| 0.1789        | 136.0 | 1496 | 0.6519          |
| 0.1156        | 137.0 | 1507 | 0.6487          |
| 0.1156        | 138.0 | 1518 | 0.6491          |
| 0.1156        | 139.0 | 1529 | 0.6479          |
| 0.1156        | 140.0 | 1540 | 0.6495          |
| 0.1156        | 141.0 | 1551 | 0.6495          |
| 0.1156        | 142.0 | 1562 | 0.6532          |
| 0.1156        | 143.0 | 1573 | 0.6478          |
| 0.1156        | 144.0 | 1584 | 0.6444          |
| 0.1156        | 145.0 | 1595 | 0.6467          |
| 0.1156        | 146.0 | 1606 | 0.6491          |
| 0.1156        | 147.0 | 1617 | 0.6492          |
| 0.1156        | 148.0 | 1628 | 0.6506          |
| 0.1156        | 149.0 | 1639 | 0.6526          |
| 0.1156        | 150.0 | 1650 | 0.6514          |
| 0.1156        | 151.0 | 1661 | 0.6461          |
| 0.1156        | 152.0 | 1672 | 0.6470          |
| 0.1156        | 153.0 | 1683 | 0.6512          |
| 0.1156        | 154.0 | 1694 | 0.6531          |
| 0.1156        | 155.0 | 1705 | 0.6499          |
| 0.1156        | 156.0 | 1716 | 0.6459          |
| 0.1156        | 157.0 | 1727 | 0.6478          |
| 0.1156        | 158.0 | 1738 | 0.6493          |
| 0.1156        | 159.0 | 1749 | 0.6508          |
| 0.1156        | 160.0 | 1760 | 0.6526          |
| 0.1156        | 161.0 | 1771 | 0.6534          |
| 0.1156        | 162.0 | 1782 | 0.6525          |
| 0.1156        | 163.0 | 1793 | 0.6507          |
| 0.1156        | 164.0 | 1804 | 0.6510          |
| 0.1156        | 165.0 | 1815 | 0.6508          |
| 0.1156        | 166.0 | 1826 | 0.6496          |
| 0.1156        | 167.0 | 1837 | 0.6471          |
| 0.1156        | 168.0 | 1848 | 0.6458          |
| 0.1156        | 169.0 | 1859 | 0.6481          |
| 0.1156        | 170.0 | 1870 | 0.6498          |
| 0.1156        | 171.0 | 1881 | 0.6513          |
| 0.1156        | 172.0 | 1892 | 0.6507          |
| 0.1156        | 173.0 | 1903 | 0.6502          |
| 0.1156        | 174.0 | 1914 | 0.6488          |
| 0.1156        | 175.0 | 1925 | 0.6477          |
| 0.1156        | 176.0 | 1936 | 0.6465          |
| 0.1156        | 177.0 | 1947 | 0.6459          |
| 0.1156        | 178.0 | 1958 | 0.6456          |
| 0.1156        | 179.0 | 1969 | 0.6478          |
| 0.1156        | 180.0 | 1980 | 0.6464          |
| 0.1156        | 181.0 | 1991 | 0.6449          |
| 0.0913        | 182.0 | 2002 | 0.6453          |
| 0.0913        | 183.0 | 2013 | 0.6469          |
| 0.0913        | 184.0 | 2024 | 0.6485          |
| 0.0913        | 185.0 | 2035 | 0.6494          |
| 0.0913        | 186.0 | 2046 | 0.6505          |
| 0.0913        | 187.0 | 2057 | 0.6511          |
| 0.0913        | 188.0 | 2068 | 0.6499          |
| 0.0913        | 189.0 | 2079 | 0.6497          |
| 0.0913        | 190.0 | 2090 | 0.6494          |
| 0.0913        | 191.0 | 2101 | 0.6493          |
| 0.0913        | 192.0 | 2112 | 0.6495          |
| 0.0913        | 193.0 | 2123 | 0.6496          |
| 0.0913        | 194.0 | 2134 | 0.6493          |
| 0.0913        | 195.0 | 2145 | 0.6493          |
| 0.0913        | 196.0 | 2156 | 0.6493          |
| 0.0913        | 197.0 | 2167 | 0.6492          |
| 0.0913        | 198.0 | 2178 | 0.6493          |
| 0.0913        | 199.0 | 2189 | 0.6492          |
| 0.0913        | 200.0 | 2200 | 0.6492          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "islamic_qa", "results": []}]}
transformers pytorch tensorboard gpt2 text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

islamic\_qa
===========


This model is a fine-tuned version of Huzaifa30/islamic\_qa on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6492


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3