long_T5_sum_approach
This model is a fine-tuned version of google/long-t5-tglobal-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.9110
- Rouge1: 0.1621
- Rouge2: 0.032
- Rougel: 0.1254
- Rougelsum: 0.126
- Gen Len: 20.0
- Bleu: 0.0
- Precisions: 0.051
- Brevity Penalty: 0.5104
- Length Ratio: 0.5979
- Translation Length: 730.0
- Reference Length: 1221.0
- Precision: 0.84
- Recall: 0.8425
- F1: 0.8411
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1)
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 12
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Precision | Recall | F1 | Hashcode |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 7 | 25.6156 | 0.2003 | 0.0566 | 0.1655 | 0.1653 | 20.0 | 0.0197 | 0.072 | 0.5371 | 0.6167 | 753.0 | 1221.0 | 0.8574 | 0.8505 | 0.8538 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 2.0 | 14 | 23.0400 | 0.1953 | 0.0521 | 0.1616 | 0.1611 | 20.0 | 0.0186 | 0.0701 | 0.5302 | 0.6118 | 747.0 | 1221.0 | 0.8566 | 0.8497 | 0.8531 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 3.0 | 21 | 20.9401 | 0.1905 | 0.0494 | 0.157 | 0.1574 | 20.0 | 0.0145 | 0.0629 | 0.5394 | 0.6183 | 755.0 | 1221.0 | 0.8581 | 0.8507 | 0.8543 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 4.0 | 28 | 19.2980 | 0.1983 | 0.0521 | 0.1626 | 0.1635 | 20.0 | 0.0157 | 0.0682 | 0.5337 | 0.6143 | 750.0 | 1221.0 | 0.8595 | 0.8511 | 0.8552 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 5.0 | 35 | 17.7768 | 0.2064 | 0.0605 | 0.1704 | 0.1719 | 20.0 | 0.0217 | 0.0758 | 0.5302 | 0.6118 | 747.0 | 1221.0 | 0.8612 | 0.8524 | 0.8567 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 6.0 | 42 | 16.2705 | 0.2071 | 0.0636 | 0.1691 | 0.1699 | 20.0 | 0.0273 | 0.0818 | 0.5313 | 0.6126 | 748.0 | 1221.0 | 0.8599 | 0.851 | 0.8553 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 7.0 | 49 | 14.6608 | 0.2018 | 0.0607 | 0.1676 | 0.1689 | 20.0 | 0.0263 | 0.0797 | 0.5244 | 0.6077 | 742.0 | 1221.0 | 0.8575 | 0.85 | 0.8536 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 8.0 | 56 | 12.7872 | 0.1914 | 0.0533 | 0.1564 | 0.1566 | 20.0 | 0.0263 | 0.0771 | 0.5267 | 0.6093 | 744.0 | 1221.0 | 0.8528 | 0.8469 | 0.8498 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 9.0 | 63 | 10.6116 | 0.2038 | 0.0562 | 0.1631 | 0.1637 | 20.0 | 0.0295 | 0.0836 | 0.5232 | 0.6069 | 741.0 | 1221.0 | 0.8542 | 0.8486 | 0.8513 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 10.0 | 70 | 8.3062 | 0.1963 | 0.0497 | 0.1559 | 0.1558 | 20.0 | 0.0244 | 0.0747 | 0.5244 | 0.6077 | 742.0 | 1221.0 | 0.8504 | 0.8472 | 0.8487 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 11.0 | 77 | 6.5339 | 0.1794 | 0.0401 | 0.1404 | 0.1411 | 20.0 | 0.0182 | 0.0649 | 0.5186 | 0.6036 | 737.0 | 1221.0 | 0.8448 | 0.8443 | 0.8444 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
No log | 12.0 | 84 | 5.9110 | 0.1621 | 0.032 | 0.1254 | 0.126 | 20.0 | 0.0 | 0.051 | 0.5104 | 0.5979 | 730.0 | 1221.0 | 0.84 | 0.8425 | 0.8411 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
Framework versions
- Transformers 4.53.1
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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Base model
google/long-t5-tglobal-base