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# xlm-roberta-large-finetuned-sinquad

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7559

## 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: 384
- eval_batch_size: 384
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1536
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0241        | 0.9   | 7    | 0.9477          |
| 0.8636        | 1.94  | 15   | 0.8885          |
| 0.8114        | 2.97  | 23   | 0.8461          |
| 0.7665        | 4.0   | 31   | 0.8407          |
| 0.7477        | 4.52  | 35   | 0.8349          |
| 0.8059        | 0.9   | 7    | 0.8374          |
| 0.7075        | 1.94  | 15   | 0.7968          |
| 0.6106        | 2.97  | 23   | 0.7655          |
| 0.5669        | 4.0   | 31   | 0.7620          |
| 0.5406        | 4.52  | 35   | 0.7559          |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.6.1
- Tokenizers 0.12.1

{'exact_match': 66.38719512195122, 'f1': 86.21696118141166}
{"tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-large-finetuned-sinquad", "results": []}]}
transformers pytorch xlm-roberta question-answering generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us 

xlm-roberta-large-finetuned-sinquad
===================================


This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7559


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: 384
* eval\_batch\_size: 384
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 1536
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.6.1
* Tokenizers 0.12.1


{'exact\_match': 66.38719512195122, 'f1': 86.21696118141166}



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 384
* eval\_batch\_size: 384
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 1536
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.6.1
* Tokenizers 0.12.1


{'exact\_match': 66.38719512195122, 'f1': 86.21696118141166}
TAGS
#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 384
* eval\_batch\_size: 384
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 1536
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.6.1
* Tokenizers 0.12.1


{'exact\_match': 66.38719512195122, 'f1': 86.21696118141166}