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# ukraine-war-pov

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a dataset of 15K social media posts from Ukraine manually annotated for pro-Ukrainian or pro-Russian point of view on the war.
It achieves the following results on a balanced test set (2K):
- Loss: 0.2166
- Accuracy: 0.9315
- F1: 0.9315
- Precision: 0.9315
- Recall: 0.9315
- AUC: 0.9774 (self-report)

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

The model was trained in this [notebook](https://drive.google.com/file/d/1RnT3fJTneFSczS_G_JLVqe4MydkTFiO0/view?usp=sharing).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.284         | 1.0   | 1875  | 0.1850          | 0.9295   | 0.9295 | 0.9303    | 0.9295 |
| 0.2271        | 2.0   | 3750  | 0.1551          | 0.9405   | 0.9405 | 0.9414    | 0.9405 |
| 0.2064        | 3.0   | 5625  | 0.1734          | 0.9305   | 0.9305 | 0.9311    | 0.9305 |
| 0.1842        | 4.0   | 7500  | 0.1694          | 0.9315   | 0.9315 | 0.9317    | 0.9315 |
| 0.1628        | 5.0   | 9375  | 0.1838          | 0.9435   | 0.9435 | 0.9438    | 0.9435 |
| 0.1309        | 6.0   | 11250 | 0.2074          | 0.9395   | 0.9395 | 0.9395    | 0.9395 |
| 0.1017        | 7.0   | 13125 | 0.2659          | 0.9365   | 0.9365 | 0.9365    | 0.9365 |
| 0.0778        | 8.0   | 15000 | 0.2851          | 0.94     | 0.9400 | 0.9400    | 0.94   |
| 0.0664        | 9.0   | 16875 | 0.3238          | 0.9385   | 0.9385 | 0.9387    | 0.9385 |
| 0.066         | 10.0  | 18750 | 0.3092          | 0.939    | 0.9390 | 0.9390    | 0.9390 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
{"language": ["uk", "ru"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "\u0420\u043e\u0441\u0456\u044f \u0437\u043d\u043e\u0432\u0443 \u0441\u043a\u043e\u044e\u0454 \u0432\u043e\u0454\u043d\u043d\u0456 \u0437\u043b\u043e\u0447\u0438\u043d\u0438", "example_title": "proukrainian"}, {"text": "\u0412\u0421\u0423 \u0432\u0441\u0435 \u0431\u0435\u0440\u0435\u0442 \u0441 \u0441\u043e\u0431\u043e\u0439 \u2014 \u0443\u043a\u0440\u0430\u0438\u043d\u0441\u043a\u0438\u0435 \u00ab\u0437\u0430\u0445\u0438\u0441\u0442\u043d\u0438\u043a\u0438\u00bb \u0432\u0437\u044f\u043b\u0438 \u0441\u0442\u0443\u043b\u044c\u0447\u0430\u043a \u0438\u0437 \u0410\u0440\u0442\u0435\u043c\u043e\u0432\u0441\u043a\u0430", "example_title": "prorussian"}], "model-index": [{"name": "ukraine-war-pov", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer uk ru license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #uk #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us 

ukraine-war-pov
===============


This model is a fine-tuned version of xlm-roberta-base on a dataset of 15K social media posts from Ukraine manually annotated for pro-Ukrainian or pro-Russian point of view on the war.
It achieves the following results on a balanced test set (2K):


* Loss: 0.2166
* Accuracy: 0.9315
* F1: 0.9315
* Precision: 0.9315
* Recall: 0.9315
* AUC: 0.9774 (self-report)


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


The model was trained in this notebook.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 123
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 123
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #uk #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 123
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #uk #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 123
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3