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# qamodel_distilbert

This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the subjqa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7950

## 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-06
- 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: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1563        | 1.0   | 81   | 2.6200          |
| 2.0431        | 2.0   | 162  | 2.1380          |
| 1.8432        | 3.0   | 243  | 2.0108          |
| 1.7601        | 4.0   | 324  | 1.9526          |
| 1.6957        | 5.0   | 405  | 1.9126          |
| 1.6477        | 6.0   | 486  | 1.8846          |
| 1.6173        | 7.0   | 567  | 1.8699          |
| 1.5799        | 8.0   | 648  | 1.8527          |
| 1.5749        | 9.0   | 729  | 1.8367          |
| 1.5422        | 10.0  | 810  | 1.8281          |
| 1.5353        | 11.0  | 891  | 1.8208          |
| 1.529         | 12.0  | 972  | 1.8116          |
| 1.5101        | 13.0  | 1053 | 1.8049          |
| 1.5005        | 14.0  | 1134 | 1.8018          |
| 1.4932        | 15.0  | 1215 | 1.8008          |
| 1.4895        | 16.0  | 1296 | 1.7976          |
| 1.4817        | 17.0  | 1377 | 1.7957          |
| 1.4695        | 18.0  | 1458 | 1.7950          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

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

qamodel\_distilbert
===================


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


* Loss: 1.7950


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-06
* 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: 18


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* 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: 18 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #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-06
* 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: 18 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2