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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/bart-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- arrow |
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model-index: |
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- name: bart-base-2024-10-12_13-22 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-base-2024-10-12_13-22 |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the arrow dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3413 |
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- Gen Len: 19.9988 |
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- Bertscorer-p: 0.5693 |
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- Bertscorer-r: 0.1741 |
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- Bertscorer-f1: 0.3646 |
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- Sacrebleu-score: 10.2355 |
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- Sacrebleu-precisions: [90.1056377359695, 78.84314927189703, 71.03531269978564, 65.97921118095769] |
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- Bleu-bp: 0.1347 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| |
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| 0.317 | 1.0 | 4772 | 0.2879 | 19.9998 | 0.5428 | 0.1582 | 0.3439 | 9.6993 | [87.29083507884441, 72.83089806032642, 64.20568134269375, 58.79563532531103] | 0.1386 | |
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| 0.1934 | 2.0 | 9544 | 0.2725 | 19.9995 | 0.5576 | 0.1608 | 0.3518 | 9.8295 | [88.83556675143292, 76.0723710308905, 67.15881021479623, 61.749907205015056] | 0.1351 | |
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| 0.1323 | 3.0 | 14316 | 0.2723 | 20.0 | 0.5678 | 0.1719 | 0.3627 | 10.1615 | [89.72749492127984, 77.42060052689843, 68.79285540795546, 63.42083414479146] | 0.1370 | |
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| 0.0882 | 4.0 | 19088 | 0.2759 | 20.0 | 0.5728 | 0.1722 | 0.3650 | 10.1777 | [90.45151089248067, 79.10211769585014, 70.55075573625463, 65.16963077018467] | 0.1344 | |
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| 0.061 | 5.0 | 23860 | 0.2968 | 20.0 | 0.5672 | 0.1735 | 0.3633 | 10.1992 | [89.8170208710569, 77.72758114247924, 69.35369251771922, 64.13642380028935] | 0.1366 | |
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| 0.0359 | 6.0 | 28632 | 0.3064 | 20.0 | 0.5692 | 0.1807 | 0.3681 | 10.3391 | [90.43231298215383, 79.56742387626873, 71.96627153855555, 66.84727640514376] | 0.1348 | |
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| 0.0229 | 7.0 | 33404 | 0.3159 | 19.9996 | 0.5683 | 0.1740 | 0.3641 | 10.3045 | [89.974323617517, 78.0061867507562, 69.70321593791971, 64.46675057044337] | 0.1375 | |
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| 0.0129 | 8.0 | 38176 | 0.3253 | 19.9999 | 0.5670 | 0.1722 | 0.3625 | 10.1527 | [89.83988773004178, 78.2656326826365, 70.11705905563593, 64.89062161576781] | 0.1350 | |
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| 0.0068 | 9.0 | 42948 | 0.3389 | 19.9994 | 0.5680 | 0.1729 | 0.3633 | 10.2220 | [89.96170046739762, 78.33494108730105, 70.31016985715492, 65.2346243333951] | 0.1356 | |
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| 0.0035 | 10.0 | 47720 | 0.3413 | 19.9988 | 0.5693 | 0.1741 | 0.3646 | 10.2355 | [90.1056377359695, 78.84314927189703, 71.03531269978564, 65.97921118095769] | 0.1347 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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