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# xlm-roberta-large-finetuned-augument-visquad2-3-4-2023-1

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:
- Best F1: 75.5555
- Loss: 1.9410
- Exact: 38.9950
- F1: 56.6891
- Total: 3821
- Hasans Exact: 55.8236
- Hasans F1: 81.3075
- Hasans Total: 2653
- Noans Exact: 0.7705
- Noans F1: 0.7705
- Noans Total: 1168
- Best Exact: 59.6179
- Best Exact Thresh: 0.6305
- Best F1 Thresh: 0.9555

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Best F1 | Validation Loss | Exact   | F1      | Total | Hasans Exact | Hasans F1 | Hasans Total | Noans Exact | Noans F1 | Noans Total | Best Exact | Best Exact Thresh | Best F1 Thresh |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 1.7058        | 1.0   | 1055  | 63.5823 | 1.3396          | 34.9385 | 53.3844 | 3821  | 50.3204      | 76.8873   | 2653         | 0.0         | 0.0      | 1168        | 50.1963    | 0.8713            | 0.9414         |
| 0.5411        | 2.0   | 2110  | 72.7630 | 1.0530          | 37.5818 | 55.6036 | 3821  | 54.1274      | 80.0834   | 2653         | 0.0         | 0.0      | 1168        | 57.3672    | 0.8339            | 0.9185         |
| 0.3807        | 3.0   | 3166  | 75.2055 | 1.0871          | 39.8063 | 56.8927 | 3821  | 57.2936      | 81.9024   | 2653         | 0.0856      | 0.0856   | 1168        | 60.2460    | 0.9277            | 0.9277         |
| 0.2835        | 4.0   | 4221  | 74.8165 | 1.1504          | 39.2044 | 56.6028 | 3821  | 56.3890      | 81.4471   | 2653         | 0.1712      | 0.1712   | 1168        | 59.1992    | 0.8443            | 0.8989         |
| 0.218         | 5.0   | 5276  | 75.1668 | 1.2219          | 39.4138 | 57.1114 | 3821  | 56.4267      | 81.9158   | 2653         | 0.7705      | 0.7705   | 1168        | 59.6179    | 0.7805            | 0.9053         |
| 0.1721        | 6.0   | 6332  | 75.9461 | 1.4275          | 39.5185 | 57.1022 | 3821  | 56.3513      | 81.6765   | 2653         | 1.2842      | 1.2842   | 1168        | 60.1675    | 0.7748            | 0.9764         |
| 0.1325        | 7.0   | 7387  | 75.7665 | 1.4863          | 39.5708 | 57.0923 | 3821  | 56.3890      | 81.6245   | 2653         | 1.3699      | 1.3699   | 1168        | 60.2722    | 0.7701            | 0.9771         |
| 0.1054        | 8.0   | 8443  | 75.6723 | 1.7071          | 38.4978 | 56.5420 | 3821  | 55.2582      | 81.2466   | 2653         | 0.4281      | 0.4281   | 1168        | 59.2515    | 0.6401            | 0.9894         |
| 0.0858        | 9.0   | 9498  | 75.4763 | 1.8596          | 38.6025 | 56.5256 | 3821  | 55.4090      | 81.2229   | 2653         | 0.4281      | 0.4281   | 1168        | 59.2515    | 0.6451            | 0.9918         |
| 0.0704        | 10.0  | 10550 | 75.5555 | 1.9410          | 38.9950 | 56.6891 | 3821  | 55.8236      | 81.3075   | 2653         | 0.7705      | 0.7705   | 1168        | 59.6179    | 0.6305            | 0.9555         |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-large-finetuned-augument-visquad2-3-4-2023-1", "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-augument-visquad2-3-4-2023-1
========================================================


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


* Best F1: 75.5555
* Loss: 1.9410
* Exact: 38.9950
* F1: 56.6891
* Total: 3821
* Hasans Exact: 55.8236
* Hasans F1: 81.3075
* Hasans Total: 2653
* Noans Exact: 0.7705
* Noans F1: 0.7705
* Noans Total: 1168
* Best Exact: 59.6179
* Best Exact Thresh: 0.6305
* Best F1 Thresh: 0.9555


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2