scipaper-summary – AI Model by shed-e | AlphaNeural AI

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scipaper-summary

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text2text-generation

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text-generation-inference

endpoints_compatible

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paper-summary

This model is a fine-tuned version of t5-small on the scitldr dataset. It achieves the following results on the evaluation set:

Loss: 2.8631
Rouge1: 0.3484
Rouge2: 0.1596
Rougel: 0.2971
Rougelsum: 0.3047

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
3.0545	1.0	63	2.9939	0.3387	0.1538	0.2887	0.2957
2.7871	2.0	126	2.9360	0.3448	0.1577	0.2947	0.3019
2.7188	3.0	189	2.8977	0.3477	0.1585	0.2967	0.3035
2.6493	4.0	252	2.8837	0.3488	0.1597	0.2973	0.3046
2.6207	5.0	315	2.8690	0.3472	0.1566	0.2958	0.3033
2.5893	6.0	378	2.8668	0.3493	0.1592	0.2972	0.305
2.5494	7.0	441	2.8657	0.3486	0.1595	0.2976	0.3053
2.5554	8.0	504	2.8631	0.3484	0.1596	0.2971	0.3047

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1