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# xlm-roberta-large-finetuned-augument-visquad2-5-4-2023-1

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: 76.9467
- Loss: 2.4868
- Exact: 38.7333
- F1: 56.7311
- Total: 3821
- Hasans Exact: 55.1451
- Hasans F1: 81.0666
- Hasans Total: 2653
- Noans Exact: 1.4555
- Noans F1: 1.4555
- Noans Total: 1168
- Best Exact: 60.7694
- Best Exact Thresh: 0.8788
- Best F1 Thresh: 0.9686

## 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: 5e-06
- 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
- 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 |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 1.1009        | 1.0   | 4221  | 69.8405 | 1.2010          | 36.1685 | 54.2642 | 3821  | 52.0920      | 78.1543   | 2653         | 0.0         | 0.0      | 1168        | 55.4305    | 0.8416            | 0.9065         |
| 0.4716        | 2.0   | 8443  | 74.1358 | 1.0553          | 38.2884 | 56.0896 | 3821  | 54.9943      | 80.6326   | 2653         | 0.3425      | 0.3425   | 1168        | 58.8328    | 0.8002            | 0.9118         |
| 0.3487        | 3.0   | 12664 | 76.3875 | 1.1176          | 39.3876 | 56.5884 | 3821  | 56.6905      | 81.4641   | 2653         | 0.0856      | 0.0856   | 1168        | 61.3190    | 0.7923            | 0.9324         |
| 0.2747        | 4.0   | 16886 | 76.3938 | 1.2634          | 38.6548 | 56.3082 | 3821  | 55.6728      | 81.0982   | 2653         | 0.0         | 0.0      | 1168        | 60.4030    | 0.7414            | 0.9059         |
| 0.217         | 5.0   | 21107 | 76.5504 | 1.3581          | 39.3353 | 56.9569 | 3821  | 56.4644      | 81.8441   | 2653         | 0.4281      | 0.4281   | 1168        | 61.0050    | 0.8307            | 0.8701         |
| 0.1758        | 6.0   | 25329 | 77.2312 | 1.5473          | 39.4399 | 56.6673 | 3821  | 56.7282      | 81.5401   | 2653         | 0.1712      | 0.1712   | 1168        | 61.4761    | 0.8283            | 0.8996         |
| 0.1429        | 7.0   | 29550 | 77.2045 | 1.7840          | 38.8642 | 56.8934 | 3821  | 55.7105      | 81.6773   | 2653         | 0.5993      | 0.5993   | 1168        | 61.3190    | 0.7413            | 0.9449         |
| 0.1159        | 8.0   | 33772 | 76.6868 | 2.1179          | 38.7071 | 56.7210 | 3821  | 55.2959      | 81.2405   | 2653         | 1.0274      | 1.0274   | 1168        | 60.4292    | 0.5939            | 0.9917         |
| 0.0997        | 9.0   | 37993 | 77.1237 | 2.3697          | 38.6548 | 56.6046 | 3821  | 55.4467      | 81.2990   | 2653         | 0.5137      | 0.5137   | 1168        | 60.8479    | 0.9509            | 0.9636         |
| 0.0873        | 10.0  | 42210 | 76.9467 | 2.4868          | 38.7333 | 56.7311 | 3821  | 55.1451      | 81.0666   | 2653         | 1.4555      | 1.4555   | 1168        | 60.7694    | 0.8788            | 0.9686         |


### 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-visquad2-5-4-2023-1", "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-visquad2-5-4-2023-1
========================================================


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: 76.9467
* Loss: 2.4868
* Exact: 38.7333
* F1: 56.7311
* Total: 3821
* Hasans Exact: 55.1451
* Hasans F1: 81.0666
* Hasans Total: 2653
* Noans Exact: 1.4555
* Noans F1: 1.4555
* Noans Total: 1168
* Best Exact: 60.7694
* Best Exact Thresh: 0.8788
* Best F1 Thresh: 0.9686


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: 5e-06
* 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
* 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: 5e-06
* 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
* 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: 5e-06
* 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
* 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: 5e-06
* 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
* 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