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flan-t5-large-bottleneck-adapter-cpgQA
This model is a fine-tuned version of google/flan-t5-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1175
- Squad: {'exact_match': 74.03846153846153, 'f1': 92.73025873728763}
- Bleu: {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270}
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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Squad | Bleu |
|---|---|---|---|---|---|
| 0.4902 | 0.23 | 100 | 0.1695 | {'exact_match': 59.61538461538461, 'f1': 88.39664262292322} | {'bleu': 0.8611708764560243, 'precisions': [0.8791469194312796, 0.8657487091222031, 0.8552631578947368, 0.8448979591836735], 'brevity_penalty': 1.0, 'length_ratio': 1.0284321689683185, 'translation_length': 1266, 'reference_length': 1231} |
| 0.3577 | 0.45 | 200 | 0.3243 | {'exact_match': 47.11538461538461, 'f1': 75.97696037540817} | {'bleu': 0.44597697779640594, 'precisions': [0.9202211690363349, 0.9087779690189329, 0.8994360902255639, 0.8948979591836734], 'brevity_penalty': 0.49236704919459706, 'length_ratio': 0.5852981969486823, 'translation_length': 1266, 'reference_length': 2163} |
| 0.2751 | 0.68 | 300 | 0.1577 | {'exact_match': 69.23076923076923, 'f1': 89.48763228957931} | {'bleu': 0.8601252797928449, 'precisions': [0.8925750394944708, 0.878657487091222, 0.8656015037593985, 0.8561224489795919], 'brevity_penalty': 0.985104158338853, 'length_ratio': 0.9852140077821012, 'translation_length': 1266, 'reference_length': 1285} |
| 0.5794 | 0.9 | 400 | 0.4970 | {'exact_match': 32.69230769230769, 'f1': 67.89210636760458} | {'bleu': 0.5849757239612657, 'precisions': [0.7282780410742496, 0.693631669535284, 0.6635338345864662, 0.6387755102040816], 'brevity_penalty': 0.8599604506941122, 'length_ratio': 0.8689087165408373, 'translation_length': 1266, 'reference_length': 1457} |
| 0.2114 | 1.13 | 500 | 0.1245 | {'exact_match': 67.3076923076923, 'f1': 89.96309177836906} | {'bleu': 0.8997821698527838, 'precisions': [0.9360189573459715, 0.9285714285714286, 0.9238721804511278, 0.9204081632653062], 'brevity_penalty': 0.9704302027764995, 'length_ratio': 0.9708588957055214, 'translation_length': 1266, 'reference_length': 1304} |
| 0.1765 | 1.36 | 600 | 0.1214 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1822 | 1.58 | 700 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.14 | 1.81 | 800 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1456 | 2.04 | 900 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1172 | 2.26 | 1000 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1376 | 2.49 | 1100 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1683 | 2.71 | 1200 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.0717 | 2.94 | 1300 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1038 | 3.17 | 1400 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.0812 | 3.39 | 1500 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1887 | 3.62 | 1600 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.0824 | 3.85 | 1700 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1046 | 4.07 | 1800 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.0952 | 4.3 | 1900 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1054 | 4.52 | 2000 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1603 | 4.75 | 2100 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1643 | 4.98 | 2200 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1326 | 5.2 | 2300 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1922 | 5.43 | 2400 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.1154 | 5.66 | 2500 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
| 0.07 | 5.88 | 2600 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
