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# BERT_Answer_only

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the circa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5147
- Accuracy: 0.8221

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6408        | 1.0   | 619  | 0.5206          | 0.8119   |
| 0.4795        | 2.0   | 1238 | 0.4976          | 0.8210   |
| 0.3933        | 3.0   | 1857 | 0.5147          | 0.8221   |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0.dev20230220
- Datasets 2.10.0
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["circa"], "metrics": ["accuracy"], "model-index": [{"name": "BERT_Answer_only", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "circa", "type": "circa", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8220942567055615, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:circa license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-circa #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

BERT\_Answer\_only
==================


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


* Loss: 0.5147
* Accuracy: 0.8221


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220
* Datasets 2.10.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220
* Datasets 2.10.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-circa #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220
* Datasets 2.10.0
* Tokenizers 0.11.0