t5-finetuned-event-extract
This model is a fine-tuned version of google/flan-t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7393
- Bleu: 0.0000
- Precisions: [0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 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0.3684210526315789]
- Brevity Penalty: 0.0000
- Length Ratio: 1.9486
- Translation Length: 4700
- Reference Length: 241200
- Gen Len: 5.9091
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Gen Len |
---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 59 | 0.9967 | 0.0000 | [0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 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0.4634146341463415, 0.3684210526315789] | 0.0000 | 1.9486 | 4700 | 241200 | 5.9091 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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