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# bert-base-uncased-finetuned-squad

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the SQuAD1.1 dataset. It was trained through Transformers' example Colab notebook on Question Answering, available [here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
It achieves the following results on the evaluation set:
- Loss: 1.0780

## 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. They are equal to the ones used to fine-tune [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for QA:
- learning_rate: 2e-05
- 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0706        | 1.0   | 5533  | 1.0250          |
| 0.7899        | 2.0   | 11066 | 1.0356          |
| 0.5991        | 3.0   | 16599 | 1.0780          |


### Validation results

|    EM    |   F1    |
|:--------:|:-------:|
| 80.3690  | 88.0110 |


### Framework versions

- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "bert-base-uncased-finetuned-squad", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]}
transformers pytorch bert question-answering generated_from_trainer dataset:squad license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 

bert-base-uncased-finetuned-squad
=================================


This model is a fine-tuned version of bert-base-uncased on the SQuAD1.1 dataset. It was trained through Transformers' example Colab notebook on Question Answering, available here.
It achieves the following results on the evaluation set:


* Loss: 1.0780


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. They are equal to the ones used to fine-tune distilbert-base-uncased for QA:


* learning\_rate: 2e-05
* 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: 3


### Training results



### Validation results



### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training. They are equal to the ones used to fine-tune distilbert-base-uncased for QA:


* learning\_rate: 2e-05
* 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: 3 ### Training results ### Validation results ### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training. They are equal to the ones used to fine-tune distilbert-base-uncased for QA:


* learning\_rate: 2e-05
* 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: 3 ### Training results ### Validation results ### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training. They are equal to the ones used to fine-tune distilbert-base-uncased for QA:


* learning\_rate: 2e-05
* 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: 3### Training results### Validation results### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3