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# distilbert-base-uncased-distilled-squad_qa_model

This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the subjqa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9380

## 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: 1e-07
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1556        | 1.0   | 32   | 4.1242          |
| 4.0411        | 2.0   | 64   | 4.0582          |
| 3.9828        | 3.0   | 96   | 3.9948          |
| 3.9068        | 4.0   | 128  | 3.9378          |
| 3.8152        | 5.0   | 160  | 3.8835          |
| 3.7906        | 6.0   | 192  | 3.8329          |
| 3.7543        | 7.0   | 224  | 3.7842          |
| 3.7173        | 8.0   | 256  | 3.7377          |
| 3.6717        | 9.0   | 288  | 3.6958          |
| 3.6219        | 10.0  | 320  | 3.6559          |
| 3.587         | 11.0  | 352  | 3.6185          |
| 3.6111        | 12.0  | 384  | 3.5808          |
| 3.5374        | 13.0  | 416  | 3.5483          |
| 3.4506        | 14.0  | 448  | 3.5175          |
| 3.4286        | 15.0  | 480  | 3.4873          |
| 3.4021        | 16.0  | 512  | 3.4596          |
| 3.432         | 17.0  | 544  | 3.4328          |
| 3.3235        | 18.0  | 576  | 3.4079          |
| 3.3627        | 19.0  | 608  | 3.3841          |
| 3.323         | 20.0  | 640  | 3.3615          |
| 3.3127        | 21.0  | 672  | 3.3389          |
| 3.2635        | 22.0  | 704  | 3.3199          |
| 3.2542        | 23.0  | 736  | 3.3013          |
| 3.2302        | 24.0  | 768  | 3.2846          |
| 3.1699        | 25.0  | 800  | 3.2676          |
| 3.2333        | 26.0  | 832  | 3.2516          |
| 3.2204        | 27.0  | 864  | 3.2364          |
| 3.1809        | 28.0  | 896  | 3.2218          |
| 3.1739        | 29.0  | 928  | 3.2082          |
| 3.1966        | 30.0  | 960  | 3.1950          |
| 3.1513        | 31.0  | 992  | 3.1826          |
| 3.135         | 32.0  | 1024 | 3.1713          |
| 3.1253        | 33.0  | 1056 | 3.1599          |
| 3.0768        | 34.0  | 1088 | 3.1498          |
| 3.1031        | 35.0  | 1120 | 3.1394          |
| 3.064         | 36.0  | 1152 | 3.1293          |
| 3.0391        | 37.0  | 1184 | 3.1200          |
| 3.0701        | 38.0  | 1216 | 3.1117          |
| 3.0787        | 39.0  | 1248 | 3.1032          |
| 3.0423        | 40.0  | 1280 | 3.0956          |
| 3.0214        | 41.0  | 1312 | 3.0875          |
| 3.0289        | 42.0  | 1344 | 3.0804          |
| 2.9667        | 43.0  | 1376 | 3.0736          |
| 3.0341        | 44.0  | 1408 | 3.0671          |
| 3.0098        | 45.0  | 1440 | 3.0606          |
| 3.0202        | 46.0  | 1472 | 3.0544          |
| 2.9598        | 47.0  | 1504 | 3.0490          |
| 2.9734        | 48.0  | 1536 | 3.0430          |
| 2.9381        | 49.0  | 1568 | 3.0375          |
| 2.9444        | 50.0  | 1600 | 3.0328          |
| 2.9357        | 51.0  | 1632 | 3.0280          |
| 2.9453        | 52.0  | 1664 | 3.0237          |
| 2.9906        | 53.0  | 1696 | 3.0191          |
| 2.934         | 54.0  | 1728 | 3.0148          |
| 2.9076        | 55.0  | 1760 | 3.0110          |
| 2.9874        | 56.0  | 1792 | 3.0070          |
| 2.9682        | 57.0  | 1824 | 3.0032          |
| 2.9287        | 58.0  | 1856 | 2.9994          |
| 2.9575        | 59.0  | 1888 | 2.9956          |
| 2.8618        | 60.0  | 1920 | 2.9926          |
| 2.9614        | 61.0  | 1952 | 2.9893          |
| 2.9463        | 62.0  | 1984 | 2.9861          |
| 2.8927        | 63.0  | 2016 | 2.9834          |
| 2.9048        | 64.0  | 2048 | 2.9805          |
| 2.9161        | 65.0  | 2080 | 2.9777          |
| 2.9117        | 66.0  | 2112 | 2.9753          |
| 2.932         | 67.0  | 2144 | 2.9729          |
| 2.9148        | 68.0  | 2176 | 2.9706          |
| 2.8919        | 69.0  | 2208 | 2.9683          |
| 2.9278        | 70.0  | 2240 | 2.9662          |
| 2.869         | 71.0  | 2272 | 2.9643          |
| 2.8844        | 72.0  | 2304 | 2.9622          |
| 2.8636        | 73.0  | 2336 | 2.9603          |
| 2.8734        | 74.0  | 2368 | 2.9585          |
| 2.8934        | 75.0  | 2400 | 2.9569          |
| 2.86          | 76.0  | 2432 | 2.9551          |
| 2.8366        | 77.0  | 2464 | 2.9539          |
| 2.8887        | 78.0  | 2496 | 2.9522          |
| 2.8632        | 79.0  | 2528 | 2.9511          |
| 2.8691        | 80.0  | 2560 | 2.9496          |
| 2.8597        | 81.0  | 2592 | 2.9484          |
| 2.8775        | 82.0  | 2624 | 2.9473          |
| 2.8491        | 83.0  | 2656 | 2.9461          |
| 2.8639        | 84.0  | 2688 | 2.9450          |
| 2.8659        | 85.0  | 2720 | 2.9443          |
| 2.8557        | 86.0  | 2752 | 2.9433          |
| 2.8188        | 87.0  | 2784 | 2.9423          |
| 2.8896        | 88.0  | 2816 | 2.9416          |
| 2.8102        | 89.0  | 2848 | 2.9409          |
| 2.8452        | 90.0  | 2880 | 2.9403          |
| 2.8437        | 91.0  | 2912 | 2.9399          |
| 2.8193        | 92.0  | 2944 | 2.9397          |
| 2.8645        | 93.0  | 2976 | 2.9391          |
| 2.8745        | 94.0  | 3008 | 2.9388          |
| 2.8568        | 95.0  | 3040 | 2.9385          |
| 2.8832        | 96.0  | 3072 | 2.9382          |
| 2.8801        | 97.0  | 3104 | 2.9382          |
| 2.8488        | 98.0  | 3136 | 2.9383          |
| 2.8233        | 99.0  | 3168 | 2.9380          |
| 2.8505        | 100.0 | 3200 | 2.9380          |


### Framework versions

- Transformers 4.28.0
- Pytorch 1.13.0a0+d321be6
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["subjqa"], "model-index": [{"name": "distilbert-base-uncased-distilled-squad_qa_model", "results": []}]}
transformers pytorch tensorboard distilbert question-answering generated_from_trainer dataset:subjqa license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-subjqa #license-apache-2.0 #endpoints_compatible #region-us 

distilbert-base-uncased-distilled-squad\_qa\_model
==================================================


This model is a fine-tuned version of distilbert-base-uncased-distilled-squad on the subjqa dataset.
It achieves the following results on the evaluation set:


* Loss: 2.9380


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: 1e-07
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0a0+d321be6
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-07
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0a0+d321be6
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-subjqa #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-07
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0a0+d321be6
* Datasets 2.12.0
* Tokenizers 0.13.3