
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlm-roberta-large-finetuned-sinquad-v2

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7850

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5061        | 0.99  | 23   | 1.3749          |
| 0.8976        | 1.98  | 46   | 0.8803          |
| 0.7572        | 2.97  | 69   | 0.7758          |
| 0.6854        | 4.0   | 93   | 0.7380          |
| 0.5903        | 4.99  | 116  | 0.7158          |
| 0.5114        | 5.98  | 139  | 0.7311          |
| 0.4291        | 6.97  | 162  | 0.7533          |
| 0.4113        | 8.0   | 186  | 0.7650          |
| 0.3564        | 8.99  | 209  | 0.7734          |
| 0.3516        | 9.89  | 230  | 0.7850          |


### Framework versions

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

{'exact_match': 67.75914634146342, 'f1': 86.42992384115712}
{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-large-finetuned-sinquad-v2", "results": []}]}
transformers pytorch xlm-roberta question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

xlm-roberta-large-finetuned-sinquad-v2
======================================


This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7850


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: 128
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* 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': 67.75914634146342, 'f1': 86.42992384115712}



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* 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': 67.75914634146342, 'f1': 86.42992384115712}
TAGS
#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* 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': 67.75914634146342, 'f1': 86.42992384115712}