readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - bleu
  model-index:
  - name: mbart-en-to-amr
    results: []
  language:
  - en
  inference: false
  ---
  
  <!-- 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. -->
  
  # MBART to "translate" English 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: 0.2460
  - Accuracy: 0.9318
  - Bleu: 71.1803
  - Smatch Precision: 0.9014
  - Smatch Recall: 0.9123
  - Smatch Fscore: 0.9068
  
  ## 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: 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: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Bleu    | Smatch Precision | Smatch Recall | Smatch Fscore | Ratio Invalid Amrs |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:----------------:|:-------------:|:-------------:|:------------------:|
  | 1.4665        | 0.23  | 100  | 1.1554          | 0.7630   | 56.3102 | 0.4              | 0.5929        | 0.4777        | 93.6121            |
  | 0.9378        | 0.46  | 200  | 0.7697          | 0.8204   | 62.5201 | 0.5654           | 0.6718        | 0.6140        | 94.3089            |
  | 0.7219        | 0.69  | 300  | 0.5224          | 0.8683   | 65.1696 | 0.8024           | 0.8407        | 0.8211        | 93.7282            |
  | 0.6117        | 0.92  | 400  | 0.4601          | 0.8768   | 66.5817 | 0.7997           | 0.8399        | 0.8193        | 94.2509            |
  | 0.4869        | 1.15  | 500  | 0.4013          | 0.8887   | 67.5473 | 0.8514           | 0.8691        | 0.8602        | 92.7991            |
  | 0.4378        | 1.38  | 600  | 0.3556          | 0.9022   | 68.9795 | 0.8111           | 0.8477        | 0.8290        | 91.4053            |
  | 0.4096        | 1.61  | 700  | 0.3538          | 0.9040   | 68.7648 | 0.8632           | 0.8752        | 0.8692        | 92.7410            |
  | 0.3873        | 1.84  | 800  | 0.3233          | 0.9049   | 68.7393 | 0.8789           | 0.8869        | 0.8829        | 92.5668            |
  | 0.3104        | 2.07  | 900  | 0.2999          | 0.9153   | 69.5170 | 0.8757           | 0.8908        | 0.8832        | 90.7085            |
  | 0.2948        | 2.3   | 1000 | 0.3328          | 0.9024   | 68.7774 | 0.8841           | 0.8911        | 0.8876        | 93.2056            |
  | 0.2967        | 2.53  | 1100 | 0.2760          | 0.9209   | 69.8172 | 0.8259           | 0.8773        | 0.8508        | 89.8374            |
  | 0.3253        | 2.76  | 1200 | 0.2677          | 0.9226   | 70.0171 | 0.8817           | 0.8973        | 0.8894        | 89.4309            |
  | 0.2993        | 2.99  | 1300 | 0.2708          | 0.9232   | 70.0466 | 0.8257           | 0.8773        | 0.8507        | 89.8374            |
  | 0.2386        | 3.23  | 1400 | 0.2619          | 0.9255   | 70.3814 | 0.8775           | 0.8885        | 0.8830        | 89.5470            |
  | 0.249         | 3.46  | 1500 | 0.2685          | 0.9224   | 70.2612 | 0.8864           | 0.8929        | 0.8896        | 90.7666            |
  | 0.2344        | 3.69  | 1600 | 0.2545          | 0.9272   | 70.8318 | 0.8945           | 0.9077        | 0.9010        | 90.2439            |
  | 0.2542        | 3.92  | 1700 | 0.2515          | 0.9271   | 70.7093 | 0.8876           | 0.9009        | 0.8942        | 89.8955            |
  | 0.2009        | 4.15  | 1800 | 0.2681          | 0.9221   | 70.2550 | 0.9147           | 0.9161        | 0.9154        | 91.2311            |
  | 0.2055        | 4.38  | 1900 | 0.2709          | 0.9214   | 70.4715 | 0.8998           | 0.9044        | 0.9021        | 91.3473            |
  | 0.2251        | 4.61  | 2000 | 0.2529          | 0.9275   | 70.9192 | 0.8907           | 0.9024        | 0.8965        | 90.1858            |
  | 0.2015        | 4.84  | 2100 | 0.2480          | 0.9294   | 71.1830 | 0.9073           | 0.9127        | 0.9100        | 90.1278            |
  | 0.1921        | 5.07  | 2200 | 0.2460          | 0.9318   | 71.1803 | 0.9014           | 0.9123        | 0.9068        | 89.6632            |
  | 0.1741        | 5.3   | 2300 | 0.2472          | 0