readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: xlm-roberta-en-ru-emoji-v2
    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-en-ru-emoji-v2
  
  This model is a fine-tuned version of [DeepPavlov/xlm-roberta-large-en-ru](https://huggingface.co/DeepPavlov/xlm-roberta-large-en-ru) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3356
  - Accuracy: 0.3102
  
  ## 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: 2e-05
  - train_batch_size: 96
  - eval_batch_size: 96
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 0.4   | 200  | 3.0592          | 0.1204   |
  | No log        | 0.81  | 400  | 2.5356          | 0.2480   |
  | 2.6294        | 1.21  | 600  | 2.4570          | 0.2569   |
  | 2.6294        | 1.62  | 800  | 2.3332          | 0.2832   |
  | 1.9286        | 2.02  | 1000 | 2.3354          | 0.2803   |
  | 1.9286        | 2.42  | 1200 | 2.3610          | 0.2881   |
  | 1.9286        | 2.83  | 1400 | 2.3004          | 0.2973   |
  | 1.7312        | 3.23  | 1600 | 2.3619          | 0.3026   |
  | 1.7312        | 3.64  | 1800 | 2.3596          | 0.3032   |
  | 1.5816        | 4.04  | 2000 | 2.2972          | 0.3072   |
  | 1.5816        | 4.44  | 2200 | 2.3077          | 0.3073   |
  | 1.5816        | 4.85  | 2400 | 2.3356          | 0.3102   |
  
  
  ### Framework versions
  
  - Transformers 4.12.3
  - Pytorch 1.9.1
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  