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# xlm-roberta-base-ukraine-war-official-v2

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4596
- Accuracy: 0.7875
- F1: 0.7842
- Precision: 0.8062
- Recall: 0.7875

## 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-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 2402
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.447         | 1.0   | 1875  | 0.3923          | 0.833    | 0.8329 | 0.8339    | 0.833  |
| 0.3818        | 2.0   | 3750  | 0.3735          | 0.835    | 0.8350 | 0.8352    | 0.835  |
| 0.3548        | 3.0   | 5625  | 0.3966          | 0.841    | 0.8404 | 0.8465    | 0.841  |
| 0.3182        | 4.0   | 7500  | 0.4211          | 0.8375   | 0.8374 | 0.8385    | 0.8375 |
| 0.2907        | 5.0   | 9375  | 0.4874          | 0.8245   | 0.8236 | 0.8315    | 0.8245 |
| 0.2732        | 6.0   | 11250 | 0.5486          | 0.837    | 0.8364 | 0.8424    | 0.837  |
| 0.2318        | 7.0   | 13125 | 0.5750          | 0.8345   | 0.8339 | 0.8398    | 0.8345 |
| 0.2012        | 8.0   | 15000 | 0.6026          | 0.841    | 0.8408 | 0.8430    | 0.841  |
| 0.1769        | 9.0   | 16875 | 0.6859          | 0.8405   | 0.8401 | 0.8436    | 0.8405 |
| 0.1379        | 10.0  | 18750 | 0.7072          | 0.84     | 0.8398 | 0.8420    | 0.8400 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "xlm-roberta-base-ukraine-war-official-v2", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-ukraine-war-official-v2
========================================


This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4596
* Accuracy: 0.7875
* F1: 0.7842
* Precision: 0.8062
* Recall: 0.7875


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-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 2402
* 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 4.28.0
* 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: 2402
* 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 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #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: 2402
* 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 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #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: 2402
* 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 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3