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# xlm-roberta-large-finetuned-augument-visquad2-2-4-2023-3

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: 76.3263
- Loss: 2.9101
- Exact: 41.0887
- F1: 58.6813
- Total: 3821
- Hasans Exact: 56.0498
- Hasans F1: 81.3876
- Hasans Total: 2653
- Noans Exact: 7.1062
- Noans F1: 7.1062
- Noans Total: 1168
- Best Exact: 60.3769
- Best Exact Thresh: 0.7798
- Best F1 Thresh: 0.9874

## 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: 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 |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 0.9242        | 1.0   | 2807  | 69.6410 | 1.0239          | 37.3201 | 55.1119 | 3821  | 53.7505      | 79.3752   | 2653         | 0.0         | 0.0      | 1168        | 55.0118    | 0.8222            | 0.8968         |
| 0.3756        | 2.0   | 5615  | 73.7526 | 1.0092          | 38.8642 | 55.8953 | 3821  | 55.9744      | 80.5035   | 2653         | 0.0         | 0.0      | 1168        | 59.4085    | 0.9128            | 0.9611         |
| 0.2595        | 3.0   | 8423  | 75.1395 | 1.0121          | 39.7278 | 56.5553 | 3821  | 57.1806      | 81.4165   | 2653         | 0.0856      | 0.0856   | 1168        | 60.6386    | 0.8138            | 0.9174         |
| 0.185         | 4.0   | 11231 | 75.2011 | 1.2309          | 39.2306 | 56.7010 | 3821  | 56.2005      | 81.3625   | 2653         | 0.6849      | 0.6849   | 1168        | 59.7749    | 0.7215            | 0.8729         |
| 0.1336        | 5.0   | 14038 | 75.0330 | 1.4052          | 38.4454 | 56.1488 | 3821  | 55.2582      | 80.7556   | 2653         | 0.2568      | 0.2568   | 1168        | 59.4085    | 0.6660            | 0.8646         |
| 0.0976        | 6.0   | 16846 | 75.4976 | 1.6109          | 38.5763 | 56.1952 | 3821  | 55.4467      | 80.8224   | 2653         | 0.2568      | 0.2568   | 1168        | 59.8534    | 0.6631            | 0.9605         |
| 0.072         | 7.0   | 19654 | 76.0690 | 1.9673          | 39.5970 | 56.9041 | 3821  | 56.0874      | 81.0142   | 2653         | 2.1404      | 2.1404   | 1168        | 60.5862    | 0.7197            | 0.9882         |
| 0.0526        | 8.0   | 22462 | 75.3652 | 2.2945          | 38.8903 | 56.5382 | 3821  | 55.3336      | 80.7511   | 2653         | 1.5411      | 1.5411   | 1168        | 59.8273    | 0.6659            | 0.9573         |
| 0.0389        | 9.0   | 25269 | 76.0674 | 2.6609          | 42.5281 | 59.8494 | 3821  | 56.0121      | 80.9591   | 2653         | 11.9007     | 11.9007  | 1168        | 60.4292    | 0.6494            | 0.9632         |
| 0.0291        | 10.0  | 28070 | 76.3263 | 2.9101          | 41.0887 | 58.6813 | 3821  | 56.0498      | 81.3876   | 2653         | 7.1062      | 7.1062   | 1168        | 60.3769    | 0.7798            | 0.9874         |


### 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-visquad2-2-4-2023-3", "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-visquad2-2-4-2023-3
========================================================


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: 76.3263
* Loss: 2.9101
* Exact: 41.0887
* F1: 58.6813
* Total: 3821
* Hasans Exact: 56.0498
* Hasans F1: 81.3876
* Hasans Total: 2653
* Noans Exact: 7.1062
* Noans F1: 7.1062
* Noans Total: 1168
* Best Exact: 60.3769
* Best Exact Thresh: 0.7798
* Best F1 Thresh: 0.9874


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: 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: 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: 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: 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: 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