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# distilbert-base-uncased-boolq

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2071
- Accuracy: 0.7315

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6506        | 1.0   | 531  | 0.6075          | 0.6681   |
| 0.575         | 2.0   | 1062 | 0.5816          | 0.6978   |
| 0.4397        | 3.0   | 1593 | 0.6137          | 0.7253   |
| 0.2524        | 4.0   | 2124 | 0.8124          | 0.7466   |
| 0.126         | 5.0   | 2655 | 1.1437          | 0.7370   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["boolq"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-boolq", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "boolq", "type": "boolq", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.7314984709480122}}]}]}
transformers pytorch distilbert text-classification generated_from_trainer en dataset:boolq license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-boolq
=============================


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


* Loss: 1.2071
* Accuracy: 0.7315


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3