readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - bleu
  model-index:
  - name: 5ep-1e-4-0.01n
    results: []
  language:
  - en
  - es
  - nl
  inference: false
  ---
  
  # MBART to "translate" English, Spanish and Dutch to AMR
  
  To be used with MBART text-to-AMR pipeline (not yet available). Won't work with the default MBartTokenizer so you have to be patient
  until the pipeline can become available. :-)
  
  This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3419
  - Accuracy: 0.9144
  - Bleu: 68.1405
  - Smatch Precision: 0.8371
  - Smatch Recall: 0.8762
  - Smatch Fscore: 0.8562
  
  ## 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: 0.0001
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 128
  - total_eval_batch_size: 16
  - 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 | Bleu    | Smatch Precision | Smatch Recall | Smatch Fscore | Ratio Invalid Amrs |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:----------------:|:-------------:|:-------------:|:------------------:|
  | 1.5897        | 0.08  | 100  | 1.2531          | 0.7246   | 50.9602 | 0.2941           | 0.6098        | 0.3968        | 88.5017            |
  | 1.0864        | 0.15  | 200  | 0.8545          | 0.7998   | 56.7648 | 0.4571           | 0.6898        | 0.5498        | 89.8955            |
  | 1.0151        | 0.23  | 300  | 0.6669          | 0.8362   | 61.7237 | 0.7295           | 0.8004        | 0.7633        | 96.1092            |
  | 0.7807        | 0.31  | 400  | 0.5775          | 0.8531   | 62.7221 | 0.5800           | 0.7567        | 0.6567        | 88.9082            |
  | 0.6952        | 0.38  | 500  | 0.5559          | 0.8624   | 68.7646 | 0.7433           | 0.8157        | 0.7778        | 89.9535            |
  | 0.6236        | 0.46  | 600  | 0.4977          | 0.8701   | 66.4847 | 0.7810           | 0.8249        | 0.8023        | 91.0956            |
  | 0.555         | 0.54  | 700  | 0.4997          | 0.8658   | 66.1344 | 0.7707           | 0.8303        | 0.7994        | 93.4572            |
  | 0.5361        | 0.61  | 800  | 0.4356          | 0.8855   | 66.0960 | 0.7758           | 0.8475        | 0.8101        | 91.9280            |
  | 0.6205        | 0.69  | 900  | 0.4167          | 0.8908   | 65.8466 | 0.6942           | 0.8099        | 0.7476        | 89.8761            |
  | 0.4986        | 0.77  | 1000 | 0.4278          | 0.8948   | 67.3305 | 0.7758           | 0.8394        | 0.8063        | 90.2052            |
  | 0.4914        | 0.84  | 1100 | 0.3897          | 0.8986   | 66.9845 | 0.8067           | 0.8568        | 0.8310        | 91.1537            |
  | 0.4345        | 0.92  | 1200 | 0.3907          | 0.8970   | 66.9021 | 0.8184           | 0.8675        | 0.8422        | 91.5796            |
  | 0.4428        | 1.0   | 1300 | 0.3754          | 0.9021   | 66.8784 | 0.8037           | 0.8534        | 0.8278        | 90.9988            |
  | 0.4033        | 1.07  | 1400 | 0.3766          | 0.9025   | 67.3791 | 0.8337           | 0.8695        | 0.8513        | 91.6183            |
  | 0.407         | 1.15  | 1500 | 0.3863          | 0.8984   | 66.9565 | 0.8074           | 0.8634        | 0.8345        | 91.8118            |
  | 0.3833        | 1.23  | 1600 | 0.3743          | 0.9022   | 67.4350 | 0.8262           | 0.8705        | 0.8478        | 91.8312            |
  | 0.3818        | 1.3   | 1700 | 0.3868          | 0.8960   | 66.5347 | 0.7721           | 0.8287        | 0.7994        | 87.7855            |
  | 0.3899        | 1.38  | 1800 | 0.3687          | 0.9038   | 67.2598 | 0.8204           | 0.8558        | 0.8378        | 90.7472            |
  | 0.3734        | 1.46  | 1900 | 0.3559          | 0.9075   | 68.0480 | 0.8483           | 0.8764        | 0.8622        | 90.9214            |
  | 0.403         | 1.53  | 2000 | 0.3608          | 0.9065   | 68.0309 | 0.8311           | 0.8705        | 0.8504        | 90.8633            |
  | 0.3298        | 1.61  | 2100 | 0.3524          | 0.9080   | 68.2512 | 0.8392           | 0.8652        | 0.8520        | 90.7666            |
  | 0.366         | 1.69  | 2200 | 0.3503          | 0.9095   | 70.5750 | 0.8420           | 0.8662        | 0.8539        | 90.6117            |
  | 0.3176        | 1.76  | 2300 | 0.3544          | 0.9080   | 68.5134 | 0.8502           | 0.8787        | 0.8642        | 91.0182            |
  | 0.3162        | 1.84  | 2400 | 0.3567          | 0.9065   | 69.927