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flan-t5-large-P-tuning-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: 0.1423
- Squad: {'exact_match': 59.63302752293578, 'f1': 82.39001389589451}
- Bleu: {'bleu': 0.6044954004940006, 'precisions': [0.6696606786427146, 0.6282194848824189, 0.5842839036755386, 0.5432276657060519], 'brevity_penalty': 1.0, 'length_ratio': 1.3012987012987014, 'translation_length': 1002, 'reference_length': 770}
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: 3e-05
- 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
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Squad | Bleu |
|---|---|---|---|---|---|
| 0.3136 | 1.0 | 494 | 0.1411 | {'exact_match': 57.79816513761468, 'f1': 81.81283979578464} | {'bleu': 0.5819448175214944, 'precisions': [0.6526946107784432, 0.6080627099664053, 0.5602027883396705, 0.515850144092219], 'brevity_penalty': 1.0, 'length_ratio': 1.363265306122449, 'translation_length': 1002, 'reference_length': 735} |
| 0.28 | 2.0 | 988 | 0.1437 | {'exact_match': 57.79816513761468, 'f1': 81.00462660225033} | {'bleu': 0.5717467837073172, 'precisions': [0.6417165668662674, 0.5968645016797313, 0.550063371356147, 0.5072046109510087], 'brevity_penalty': 1.0, 'length_ratio': 1.3707250341997264, 'translation_length': 1002, 'reference_length': 731} |
| 0.2351 | 3.0 | 1482 | 0.1436 | {'exact_match': 57.79816513761468, 'f1': 80.92424215489342} | {'bleu': 0.5786949268545348, 'precisions': [0.6477045908183633, 0.6035834266517357, 0.5576679340937896, 0.5144092219020173], 'brevity_penalty': 1.0, 'length_ratio': 1.3431635388739946, 'translation_length': 1002, 'reference_length': 746} |
| 0.2736 | 4.0 | 1976 | 0.1434 | {'exact_match': 57.79816513761468, 'f1': 80.98182982715996} | {'bleu': 0.5763280403149159, 'precisions': [0.6467065868263473, 0.6024636058230683, 0.5551330798479087, 0.5100864553314121], 'brevity_penalty': 1.0, 'length_ratio': 1.3449664429530201, 'translation_length': 1002, 'reference_length': 745} |
| 0.2408 | 5.0 | 2470 | 0.1415 | {'exact_match': 57.79816513761468, 'f1': 81.54539470265146} | {'bleu': 0.5849077021683632, 'precisions': [0.653692614770459, 0.6103023516237402, 0.5640050697084917, 0.5201729106628242], 'brevity_penalty': 1.0, 'length_ratio': 1.3306772908366533, 'translation_length': 1002, 'reference_length': 753} |
| 0.2513 | 6.0 | 2964 | 0.1426 | {'exact_match': 59.63302752293578, 'f1': 81.88361818381664} | {'bleu': 0.5934811323519371, 'precisions': [0.6606786427145709, 0.6181410974244121, 0.5728770595690748, 0.5302593659942363], 'brevity_penalty': 1.0, 'length_ratio': 1.323645970937913, 'translation_length': 1002, 'reference_length': 757} |
| 0.2231 | 7.0 | 3458 | 0.1419 | {'exact_match': 59.63302752293578, 'f1': 82.39001389589451} | {'bleu': 0.6044954004940006, 'precisions': [0.6696606786427146, 0.6282194848824189, 0.5842839036755386, 0.5432276657060519], 'brevity_penalty': 1.0, 'length_ratio': 1.3012987012987014, 'translation_length': 1002, 'reference_length': 770} |
| 0.2575 | 8.0 | 3952 | 0.1423 | {'exact_match': 59.63302752293578, 'f1': 82.39001389589451} | {'bleu': 0.6044954004940006, 'precisions': [0.6696606786427146, 0.6282194848824189, 0.5842839036755386, 0.5432276657060519], 'brevity_penalty': 1.0, 'length_ratio': 1.3012987012987014, 'translation_length': 1002, 'reference_length': 770} |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
