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flan-t5-large-extraction-cnndm_2000-all-hint_precision-ep2
Zekunli/flan-t5-large-extraction-cnndm_2000-all-hint_precision-ep2
flan-t5-large-extraction-cnndm_2000-all-hint_precision-ep2 is a machine learning model from Zekunli. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- Mar 6, 2023
- Updated
- Mar 6, 2023
- SHA
- 53794a07
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
flan-t5-large-extraction-cnndm_2000-all-hint_precision-ep2
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2583
- Hint Hit: 1.0595
- Hint Hit Num: 2.034
- Hint Precision: 0.3447
- Hint Bleu: 3.7517
- Num: 5.932
- Gen Len: 18.998
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: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Hint Hit | Hint Hit Num | Hint Precision | Hint Bleu | Num | Gen Len |
|---|---|---|---|---|---|---|---|---|---|
| 0.7762 | 0.8 | 200 | 2.2946 | 0.993 | 1.978 | 0.3345 | 3.6464 | 5.918 | 18.998 |
| 0.7168 | 1.6 | 400 | 2.4462 | 1.028 | 2.014 | 0.3396 | 3.6839 | 5.958 | 18.998 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- t5
- Created
- Mar 6, 2023
- Updated
- Mar 6, 2023