readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: twitter-data-xlm-roberta-base-hindi-only-memes
    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-data-xlm-roberta-base-hindi-only-memes
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4006
  - Accuracy: 0.9240
  - Precision: 0.9255
  - Recall: 0.9263
  - F1: 0.9259
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.7485        | 1.0   | 511  | 0.4062          | 0.8381   | 0.8520    | 0.8422 | 0.8417 |
  | 0.4253        | 2.0   | 1022 | 0.3195          | 0.8822   | 0.8880    | 0.8853 | 0.8851 |
  | 0.2899        | 3.0   | 1533 | 0.2994          | 0.9031   | 0.9068    | 0.9060 | 0.9049 |
  | 0.2116        | 4.0   | 2044 | 0.3526          | 0.9163   | 0.9199    | 0.9185 | 0.9187 |
  | 0.1582        | 5.0   | 2555 | 0.4031          | 0.9163   | 0.9193    | 0.9186 | 0.9187 |
  | 0.103         | 6.0   | 3066 | 0.4006          | 0.9240   | 0.9255    | 0.9263 | 0.9259 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  