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# xlm-roberta-large-finetuned-augument-visquad2-27-3-2023-3

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: 75.3631
- Loss: 2.0450
- Exact: 38.9165
- F1: 56.3720
- Total: 3821
- Hasans Exact: 55.9744
- Hasans F1: 81.1148
- Hasans Total: 2653
- Noans Exact: 0.1712
- Noans F1: 0.1712
- Noans Total: 1168
- Best Exact: 59.7749
- Best Exact Thresh: 0.5183
- Best F1 Thresh: 0.8690

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### 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 |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 0.8597        | 1.0   | 4221  | 66.4890 | 1.2255          | 36.1947 | 54.1414 | 3821  | 52.1297      | 77.9775   | 2653         | 0.0         | 0.0      | 1168        | 52.9704    | 0.8158            | 0.9074         |
| 0.4623        | 2.0   | 8443  | 70.0050 | 1.1813          | 37.8173 | 55.5970 | 3821  | 54.4666      | 80.0740   | 2653         | 0.0         | 0.0      | 1168        | 55.1950    | 0.7529            | 0.8275         |
| 0.2999        | 3.0   | 12664 | 75.0810 | 1.2417          | 39.8587 | 56.3329 | 3821  | 57.3690      | 81.0961   | 2653         | 0.0856      | 0.0856   | 1168        | 60.4030    | 0.9294            | 0.9459         |
| 0.1915        | 4.0   | 16886 | 74.7037 | 1.6500          | 38.7333 | 56.2476 | 3821  | 55.7482      | 80.9733   | 2653         | 0.0856      | 0.0856   | 1168        | 58.6496    | 0.7690            | 0.9767         |
| 0.1185        | 5.0   | 21105 | 75.3631 | 2.0450          | 38.9165 | 56.3720 | 3821  | 55.9744      | 81.1148   | 2653         | 0.1712      | 0.1712   | 1168        | 59.7749    | 0.5183            | 0.8690         |


### 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-visquad2-27-3-2023-3", "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-visquad2-27-3-2023-3
=========================================================


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: 75.3631
* Loss: 2.0450
* Exact: 38.9165
* F1: 56.3720
* Total: 3821
* Hasans Exact: 55.9744
* Hasans F1: 81.1148
* Hasans Total: 2653
* Noans Exact: 0.1712
* Noans F1: 0.1712
* Noans Total: 1168
* Best Exact: 59.7749
* Best Exact Thresh: 0.5183
* Best F1 Thresh: 0.8690


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### 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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### 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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### 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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


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