readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - xtreme
  metrics:
  - f1
  model-index:
  - name: XLM-R-fine-tuned-for-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: xtreme
        type: xtreme
        args: PAN-X.en
      metrics:
      - name: F1
        type: f1
        value: 0.8377982238973259
  ---
  
  <!-- 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-R-fine-tuned-for-ner
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5679
  - F1: 0.8378
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - 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  | Validation Loss | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.4202        | 1.0   | 2500  | 0.3449          | 0.7963 |
  | 0.2887        | 2.0   | 5000  | 0.2756          | 0.8057 |
  | 0.2309        | 3.0   | 7500  | 0.2971          | 0.8040 |
  | 0.1832        | 4.0   | 10000 | 0.3319          | 0.8167 |
  | 0.1461        | 5.0   | 12500 | 0.3958          | 0.8350 |
  | 0.114         | 6.0   | 15000 | 0.4087          | 0.8316 |
  | 0.0833        | 7.0   | 17500 | 0.4320          | 0.8361 |
  | 0.0614        | 8.0   | 20000 | 0.4885          | 0.8353 |
  | 0.039         | 9.0   | 22500 | 0.5408          | 0.8390 |
  | 0.0251        | 10.0  | 25000 | 0.5679          | 0.8378 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.9.1
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  