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## Model description

#### Finetuned on SQUAD2.0 Dataset
#### F1: 83.738696142672

Trained on single V100 GPU

Everyone is welcome to use~ 

Hope you have a nice day


## Performance

 - HasAns_exact': 77.1255060728745, 'HasAns_f1': 83.87812741260885, 'HasAns_total': 5928,
 - 'NoAns_exact': 83.59966358284272, 'NoAns_f1': 83.59966358284272, 'NoAns_total': 5945,
 - 'best_exact': 80.36721974227238, 'best_exact_thresh': 0.0, 
 - 'best_f1': 83.7386961426719, 'best_f1_thresh': 0.0,
 
 - 'exact': 80.36721974227238, 
 - 'f1': 83.738696142672, 
 - 'total': 11873

# roberta-base-finetuned-squad2-lwt

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9441


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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.871         | 1.0   | 8239  | 0.8156          |
| 0.6787        | 2.0   | 16478 | 0.8494          |
| 0.4867        | 3.0   | 24717 | 0.9441          |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6



{"tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "--license": "mit"}
transformers pytorch tensorboard roberta question-answering generated_from_trainer dataset:squad_v2 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us 

Model description
-----------------


#### Finetuned on SQUAD2.0 Dataset


#### F1: 83.738696142672


Trained on single V100 GPU


Everyone is welcome to use~


Hope you have a nice day


Performance
-----------


* HasAns\_exact': 77.1255060728745, 'HasAns\_f1': 83.87812741260885, 'HasAns\_total': 5928,
* 'NoAns\_exact': 83.59966358284272, 'NoAns\_f1': 83.59966358284272, 'NoAns\_total': 5945,
* 'best\_exact': 80.36721974227238, 'best\_exact\_thresh': 0.0,
* 'best\_f1': 83.7386961426719, 'best\_f1\_thresh': 0.0,
* 'exact': 80.36721974227238,
* 'f1': 83.738696142672,
* 'total': 11873


roberta-base-finetuned-squad2-lwt
=================================


This model is a fine-tuned version of roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9441


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: 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



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



#### Finetuned on SQUAD2.0 Dataset #### F1: 83.738696142672


Trained on single V100 GPU


Everyone is welcome to use~


Hope you have a nice day


Performance
-----------


* HasAns\_exact': 77.1255060728745, 'HasAns\_f1': 83.87812741260885, 'HasAns\_total': 5928,
* 'NoAns\_exact': 83.59966358284272, 'NoAns\_f1': 83.59966358284272, 'NoAns\_total': 5945,
* 'best\_exact': 80.36721974227238, 'best\_exact\_thresh': 0.0,
* 'best\_f1': 83.7386961426719, 'best\_f1\_thresh': 0.0,
* 'exact': 80.36721974227238,
* 'f1': 83.738696142672,
* 'total': 11873


roberta-base-finetuned-squad2-lwt
=================================


This model is a fine-tuned version of roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9441


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: 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 ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us 
 #### Finetuned on SQUAD2.0 Dataset #### F1: 83.738696142672


Trained on single V100 GPU


Everyone is welcome to use~


Hope you have a nice day


Performance
-----------


* HasAns\_exact': 77.1255060728745, 'HasAns\_f1': 83.87812741260885, 'HasAns\_total': 5928,
* 'NoAns\_exact': 83.59966358284272, 'NoAns\_f1': 83.59966358284272, 'NoAns\_total': 5945,
* 'best\_exact': 80.36721974227238, 'best\_exact\_thresh': 0.0,
* 'best\_f1': 83.7386961426719, 'best\_f1\_thresh': 0.0,
* 'exact': 80.36721974227238,
* 'f1': 83.738696142672,
* 'total': 11873


roberta-base-finetuned-squad2-lwt
=================================


This model is a fine-tuned version of roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9441


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: 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 ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us 
#### Finetuned on SQUAD2.0 Dataset#### F1: 83.738696142672


Trained on single V100 GPU


Everyone is welcome to use~


Hope you have a nice day


Performance
-----------


* HasAns\_exact': 77.1255060728745, 'HasAns\_f1': 83.87812741260885, 'HasAns\_total': 5928,
* 'NoAns\_exact': 83.59966358284272, 'NoAns\_f1': 83.59966358284272, 'NoAns\_total': 5945,
* 'best\_exact': 80.36721974227238, 'best\_exact\_thresh': 0.0,
* 'best\_f1': 83.7386961426719, 'best\_f1\_thresh': 0.0,
* 'exact': 80.36721974227238,
* 'f1': 83.738696142672,
* 'total': 11873


roberta-base-finetuned-squad2-lwt
=================================


This model is a fine-tuned version of roberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9441


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: 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