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# xlm-roberta-large-finetuned-augument-visquad2-1-4-2023-2

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: 76.7211
- Loss: 2.6463
- Exact: 38.4193
- F1: 56.9010
- Total: 3821
- Hasans Exact: 54.6551
- Hasans F1: 81.2735
- Hasans Total: 2653
- Noans Exact: 1.5411
- Noans F1: 1.5411
- Noans Total: 1168
- Best Exact: 60.2722
- Best Exact Thresh: 0.5938
- Best F1 Thresh: 0.9029

## 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: 2000
- 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 |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 1.5748        | 1.0   | 2110  | 62.9530 | 1.3457          | 34.5983 | 52.8434 | 3821  | 49.8304      | 76.1080   | 2653         | 0.0         | 0.0      | 1168        | 48.4952    | 0.7967            | 0.9001         |
| 0.5259        | 2.0   | 4221  | 73.5147 | 1.0489          | 37.9482 | 55.5987 | 3821  | 54.6551      | 80.0763   | 2653         | 0.0         | 0.0      | 1168        | 58.5972    | 0.8404            | 0.8855         |
| 0.3605        | 3.0   | 6332  | 75.9336 | 1.0857          | 39.4399 | 56.4203 | 3821  | 56.8036      | 81.2597   | 2653         | 0.0         | 0.0      | 1168        | 61.0311    | 0.8271            | 0.9440         |
| 0.2592        | 4.0   | 8443  | 75.9761 | 1.2592          | 39.2044 | 56.3936 | 3821  | 56.1628      | 80.9197   | 2653         | 0.6849      | 0.6849   | 1168        | 60.5339    | 0.8107            | 0.8616         |
| 0.1932        | 5.0   | 10553 | 76.2008 | 1.3238          | 39.3614 | 56.8951 | 3821  | 55.9367      | 81.1897   | 2653         | 1.7123      | 1.7123   | 1168        | 60.5862    | 0.7110            | 0.8943         |
| 0.1421        | 6.0   | 12664 | 76.3034 | 1.5313          | 38.1837 | 56.2592 | 3821  | 54.7305      | 80.7638   | 2653         | 0.5993      | 0.5993   | 1168        | 60.1675    | 0.7653            | 0.9318         |
| 0.1052        | 7.0   | 14775 | 76.1684 | 1.7621          | 38.2884 | 56.6387 | 3821  | 54.6928      | 81.1219   | 2653         | 1.0274      | 1.0274   | 1168        | 59.9320    | 0.8190            | 0.8802         |
| 0.0776        | 8.0   | 16886 | 76.3481 | 2.2045          | 38.1837 | 56.8339 | 3821  | 54.3159      | 81.1769   | 2653         | 1.5411      | 1.5411   | 1168        | 59.9320    | 0.7125            | 0.9312         |
| 0.0572        | 9.0   | 18996 | 76.5232 | 2.4641          | 38.5239 | 56.9164 | 3821  | 54.9190      | 81.4088   | 2653         | 1.2842      | 1.2842   | 1168        | 60.1413    | 0.6647            | 0.9936         |
| 0.0456        | 10.0  | 21100 | 76.7211 | 2.6463          | 38.4193 | 56.9010 | 3821  | 54.6551      | 81.2735   | 2653         | 1.5411      | 1.5411   | 1168        | 60.2722    | 0.5938            | 0.9029         |


### 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-visquad2-1-4-2023-2", "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-visquad2-1-4-2023-2
========================================================


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: 76.7211
* Loss: 2.6463
* Exact: 38.4193
* F1: 56.9010
* Total: 3821
* Hasans Exact: 54.6551
* Hasans F1: 81.2735
* Hasans Total: 2653
* Noans Exact: 1.5411
* Noans F1: 1.5411
* Noans Total: 1168
* Best Exact: 60.2722
* Best Exact Thresh: 0.5938
* Best F1 Thresh: 0.9029


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: 2000
* 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: 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: 2000
* 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: 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: 2000
* 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: 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: 2000
* num\_epochs: 10### Training results### Framework versions


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