readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - toydata
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-large-ner-hrl-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: toydata
        type: toydata
        args: SDN
      metrics:
      - name: Precision
        type: precision
        value: 0.9132452695465905
      - name: Recall
        type: recall
        value: 0.9205854126679462
      - name: F1
        type: f1
        value: 0.9169006511739053
      - name: Accuracy
        type: accuracy
        value: 0.9784804945824268
  ---
  
  <!-- 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-large-ner-hrl-finetuned-ner
  
  This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl) on the toydata dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0944
  - Precision: 0.9132
  - Recall: 0.9206
  - F1: 0.9169
  - Accuracy: 0.9785
  
  ## 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: 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 408  | 0.0900          | 0.8508    | 0.9303 | 0.8888 | 0.9719   |
  | 0.1087        | 2.0   | 816  | 0.0827          | 0.9043    | 0.9230 | 0.9136 | 0.9783   |
  | 0.0503        | 3.0   | 1224 | 0.0944          | 0.9132    | 0.9206 | 0.9169 | 0.9785   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  