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

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: 77.2917
- Loss: 1.3981
- Exact: 39.8063
- F1: 57.2058
- Total: 3821
- Hasans Exact: 56.6152
- Hasans F1: 81.6749
- Hasans Total: 2653
- Noans Exact: 1.6267
- Noans F1: 1.6267
- Noans Total: 1168
- Best Exact: 61.4499
- Best Exact Thresh: 0.8562
- Best F1 Thresh: 0.8563

## 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: 4
- total_train_batch_size: 32
- 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: 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 |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 1.2156        | 1.0   | 2110  | 72.5368 | 1.1646          | 37.6341 | 55.4110 | 3821  | 54.1651      | 79.7684   | 2653         | 0.0856      | 0.0856   | 1168        | 57.3672    | 0.8787            | 0.9480         |
| 0.442         | 2.0   | 4221  | 75.8888 | 1.0343          | 38.7857 | 56.3275 | 3821  | 55.8613      | 81.1261   | 2653         | 0.0         | 0.0      | 1168        | 60.6386    | 0.8815            | 0.8837         |
| 0.3067        | 3.0   | 6332  | 76.3203 | 1.1121          | 39.7540 | 56.6409 | 3821  | 57.2559      | 81.5774   | 2653         | 0.0         | 0.0      | 1168        | 61.3975    | 0.7946            | 0.9089         |
| 0.2223        | 4.0   | 8443  | 76.6216 | 1.2653          | 39.6493 | 56.7375 | 3821  | 57.0675      | 81.6789   | 2653         | 0.0856      | 0.0856   | 1168        | 61.3190    | 0.7363            | 0.9385         |
| 0.1674        | 5.0   | 10550 | 77.2917 | 1.3981          | 39.8063 | 57.2058 | 3821  | 56.6152      | 81.6749   | 2653         | 1.6267      | 1.6267   | 1168        | 61.4499    | 0.8562            | 0.8563         |


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


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: 77.2917
* Loss: 1.3981
* Exact: 39.8063
* F1: 57.2058
* Total: 3821
* Hasans Exact: 56.6152
* Hasans F1: 81.6749
* Hasans Total: 2653
* Noans Exact: 1.6267
* Noans F1: 1.6267
* Noans Total: 1168
* Best Exact: 61.4499
* Best Exact Thresh: 0.8562
* Best F1 Thresh: 0.8563


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: 4
* total\_train\_batch\_size: 32
* 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: 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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* 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: 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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* 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: 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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* 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: 5### Training results### Framework versions


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