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# xlm-roberta-large-finetuned-sent_in_news

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8872
- Accuracy: 0.7273
- F1: 0.5125

## Model description

Модель ассиметрична, реагирует на метку X в тексте новости.
Попробуйте следующие примеры:

a) Агентство X понизило рейтинг банка Fitch.                              
b) Агентство Fitch понизило рейтинг банка X.

a) Компания Финам показала рекордную прибыль, говорят аналитики компании X.
b) Компания X показала рекордную прибыль, говорят аналитики компании Финам.

## 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: 3e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 106  | 1.2526          | 0.6108   | 0.1508 |
| No log        | 2.0   | 212  | 1.1553          | 0.6648   | 0.1141 |
| No log        | 3.0   | 318  | 1.1150          | 0.6591   | 0.1247 |
| No log        | 4.0   | 424  | 1.0007          | 0.6705   | 0.1383 |
| 1.1323        | 5.0   | 530  | 0.9267          | 0.6733   | 0.2027 |
| 1.1323        | 6.0   | 636  | 1.0869          | 0.6335   | 0.4084 |
| 1.1323        | 7.0   | 742  | 1.1224          | 0.6932   | 0.4586 |
| 1.1323        | 8.0   | 848  | 1.2535          | 0.6307   | 0.3424 |
| 1.1323        | 9.0   | 954  | 1.4288          | 0.6932   | 0.4881 |
| 0.5252        | 10.0  | 1060 | 1.5856          | 0.6932   | 0.4739 |
| 0.5252        | 11.0  | 1166 | 1.7101          | 0.6733   | 0.4530 |
| 0.5252        | 12.0  | 1272 | 1.7330          | 0.6903   | 0.4750 |
| 0.5252        | 13.0  | 1378 | 1.8872          | 0.7273   | 0.5125 |
| 0.5252        | 14.0  | 1484 | 1.8797          | 0.7301   | 0.5033 |
| 0.1252        | 15.0  | 1590 | 1.9339          | 0.7330   | 0.5024 |
| 0.1252        | 16.0  | 1696 | 1.9632          | 0.7301   | 0.4967 |


### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlm-roberta-large-finetuned-sent_in_news", "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-large-finetuned-sent\_in\_news
==========================================


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


* Loss: 1.8872
* Accuracy: 0.7273
* F1: 0.5125


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


Модель ассиметрична, реагирует на метку X в тексте новости.
Попробуйте следующие примеры:


a) Агентство X понизило рейтинг банка Fitch.  

b) Агентство Fitch понизило рейтинг банка X.


a) Компания Финам показала рекордную прибыль, говорят аналитики компании X.
b) Компания X показала рекордную прибыль, говорят аналитики компании Финам.


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: 3e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16


### Training results



### Framework versions


* Transformers 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16 ### Training results ### Framework versions


* Transformers 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
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: 3e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16 ### Training results ### Framework versions


* Transformers 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: 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: 3e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16### Training results### Framework versions


* Transformers 4.11.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3