readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: finetuned_orgnames_sagemaker_multilang
    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. -->
  
  # finetuned_orgnames_sagemaker_multilang
  
  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: 5.0275
  - Spancat Precision: 0.8564
  - Spancat Recall: 0.8564
  - Spancat F1: 0.8564
  - Id P: 0.9199
  - Id R: 0.9152
  - Id F: 0.9175
  - Ind P: 0.7132
  - Ind R: 0.7132
  - Ind F: 0.7132
  - Leg P: 0.9589
  - Leg R: 0.9333
  - Leg F: 0.9459
  - Stp P: 0.9605
  - Stp R: 1.0
  - Stp F: 0.9799
  - Loc P: 0.7143
  - Loc R: 0.6818
  - Loc F: 0.6977
  - Org P: 0.7917
  - Org R: 0.7808
  - Org F: 0.7862
  - Per P: 0.5789
  - Per R: 0.5366
  - Per F: 0.5570
  - Acr P: 0.9540
  - Acr R: 0.9326
  - Acr F: 0.9432
  - Col P: 0.6875
  - Col R: 0.6875
  - Col F: 0.6875
  - Scp P: 1.0
  - Scp R: 0.7778
  - Scp F: 0.875
  - Str P: 0.9333
  - Str R: 0.9333
  - Str F: 0.9333
  - Nat P: 1.0
  - Nat R: 1.0
  - Nat F: 1.0
  - Mod P: 0.5
  - Mod R: 1.0
  - Mod F: 0.6667
  
  ## 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: 32
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 200000
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Spancat Precision | Spancat Recall | Spancat F1 | Id P   | Id R   | Id F   | Ind P  | Ind R  | Ind F  | Leg P  | Leg R  | Leg F  | Stp P  | Stp R | Stp F  | Loc P  | Loc R  | Loc F  | Org P  | Org R  | Org F  | Per P  | Per R  | Per F  | Acr P  | Acr R  | Acr F  | Col P  | Col R  | Col F  | Scp P | Scp R  | Scp F | Str P  | Str R  | Str F  | Nat P | Nat R | Nat F | Mod P  | Mod R | Mod F  |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-----:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-----:|:------:|:-----:|:------:|:------:|:------:|:-----:|:-----:|:-----:|:------:|:-----:|:------:|
  | 5.7513        | 0.02  | 5000  | 3.8256          | 0.8609            | 0.8439         | 0.8523     | 0.9153 | 0.8895 | 0.9022 | 0.7258 | 0.6977 | 0.7115 | 0.9589 | 0.9333 | 0.9459 | 0.9605 | 1.0   | 0.9799 | 0.7692 | 0.6818 | 0.7229 | 0.7612 | 0.6986 | 0.7286 | 0.6410 | 0.6098 | 0.625  | 0.9444 | 0.9551 | 0.9497 | 0.75   | 0.75   | 0.75   | 1.0   | 0.7778 | 0.875 | 0.9333 | 0.9333 | 0.9333 | 1.0   | 1.0   | 1.0   | 0.3333 | 1.0   | 0.5    |
  | 5.678         | 0.05  | 10000 | 4.7220          | 0.8671            | 0.8554         | 0.8612     | 0.9112 | 0.8972 | 0.9041 | 0.744  | 0.7209 | 0.7323 | 0.9459 | 0.9333 | 0.9396 | 0.9605 | 1.0   | 0.9799 | 0.7949 | 0.7045 | 0.7470 | 0.8028 | 0.7808 | 0.7917 | 0.6667 | 0.5854 | 0.6234 | 0.9551 | 0.9551 | 0.9551 | 0.7857 | 0.6875 | 0.7333 | 1.0   | 0.7778 | 0.875 | 0.9333 | 0.9333 | 0.9333 | 1.0   | 1.0   | 1.0   | 0.3333 | 1.0   | 0.5    |
  | 5.3084        | 0.07  | 15000 | 6.3091          | 0.8528            | 0.8439         | 0.8483     | 0.9110 | 0.8946 | 0.9027 | 0.7154 | 0.6822 | 0.6984 | 0.9722 | 0.9333 | 0.9524 | 0.9605 | 1.0   | 0.9799 | 0.6531 | 0.7273 | 0.6882 | 0.7714 | 0.7397 | 0.7552 | 0.6216 | 0.5610 | 0.5897 | 0.9765 | 0.9326 | 0.9540 | 0.7857 | 0.6875 | 0.7333 | 1.0   | 0.7778 | 0.875 | 0.9333 | 0.9333 | 0.9333 | 1.0   | 1.0   | 1.0   | 0.3333 | 1.0   | 0.5    |
  | 5.1136        | 0.1   | 20000 | 5.0275          | 0.8564            | 0.8564         | 0.8564     | 0.9199 | 0.9152 | 0.9175 | 0.7132 | 0.7132 | 0.7132 | 0.9589 | 0.9333 | 0.9459 | 0.9605 | 1.0   | 0.9799 | 0.7143 | 0.6818 | 0.6977 | 0.7917 | 0.7808 | 0.7862 | 0.5789 | 0.5366 | 0.5570 | 0.9540 | 0.9326 | 0.9432 | 0.6875 | 0.6875 | 0.6875 | 1.0   | 0.7778 | 0.875 | 0.9333 | 0.9333 | 0.9333 | 1.0   | 1.0   | 1.0   | 0.5    | 1.0   | 0.6667 |
  
  
  ### Framework versions
  
  - Transformers 4.11.0
  - Pytorch 1.9.0
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  