readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: xlm-roberta-large-finetuned-sent_in_news
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 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
  