readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: twitter_RoBERTa_base_sentence_itr0_1e-05_all_01_03_2022-13_53_11
    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. -->
  
  # twitter_RoBERTa_base_sentence_itr0_1e-05_all_01_03_2022-13_53_11
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4118
  - Accuracy: 0.8446
  - F1: 0.8968
  - Precision: 0.8740
  - Recall: 0.9207
  
  ## 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-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 390  | 0.3532          | 0.8451   | 0.8990 | 0.8997    | 0.8983 |
  | 0.4111        | 2.0   | 780  | 0.3381          | 0.8561   | 0.9080 | 0.8913    | 0.9253 |
  | 0.3031        | 3.0   | 1170 | 0.3490          | 0.8537   | 0.9034 | 0.9152    | 0.8919 |
  | 0.2408        | 4.0   | 1560 | 0.3562          | 0.8671   | 0.9148 | 0.9       | 0.9300 |
  | 0.2408        | 5.0   | 1950 | 0.3725          | 0.8659   | 0.9131 | 0.9074    | 0.9189 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  