readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: electra-large-discriminator-nli-efl-tweeteval
    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. -->
  
  # electra-large-discriminator-nli-efl-tweeteval
  
  This model is a fine-tuned version of [ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli](https://huggingface.co/ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli) on the None dataset.
  It achieves the following results on the evaluation set:
  - Accuracy: 0.7943
  - F1: 0.7872
  - Loss: 0.3004
  
  ## 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: 1e-06
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss |
  |:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
  | 0.4384        | 1.0   | 163  | 0.7444   | 0.7308 | 0.3962          |
  | 0.3447        | 2.0   | 326  | 0.7659   | 0.7552 | 0.3410          |
  | 0.3057        | 3.0   | 489  | 0.7750   | 0.7688 | 0.3234          |
  | 0.287         | 4.0   | 652  | 0.7857   | 0.7779 | 0.3069          |
  | 0.2742        | 5.0   | 815  | 0.7887   | 0.7822 | 0.3030          |
  | 0.2676        | 6.0   | 978  | 0.7939   | 0.7851 | 0.2982          |
  | 0.2585        | 7.0   | 1141 | 0.7909   | 0.7822 | 0.3002          |
  | 0.2526        | 8.0   | 1304 | 0.7943   | 0.7876 | 0.3052          |
  | 0.2479        | 9.0   | 1467 | 0.7939   | 0.7847 | 0.2997          |
  | 0.2451        | 10.0  | 1630 | 0.7956   | 0.7873 | 0.3014          |
  | 0.2397        | 11.0  | 1793 | 0.7943   | 0.7872 | 0.3004          |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.12.0.dev20220417
  - Datasets 2.1.0
  - Tokenizers 0.10.3
  