lesson-summarization

Ajani/lesson-summarization

lesson-summarization is a machine learning model from Ajani. 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. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: May 23, 2023
Updated: Sep 1, 2023
SHA: 4147a9f8

Model card

From the Hugging Face model README

lesson-summarization

This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.0801

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: 200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.8198	3.12	200	2.8048
2.5358	6.25	400	2.6645
2.333	9.38	600	2.6123
2.2096	12.5	800	2.5807
2.0783	15.62	1000	2.5703
1.9919	18.75	1200	2.5653
1.89	21.88	1400	2.5602
1.7865	25.0	1600	2.5650
1.7149	28.12	1800	2.5812
1.6651	31.25	2000	2.5813
1.5662	34.38	2200	2.5997
1.5333	37.5	2400	2.6097
1.4336	40.62	2600	2.6389
1.3986	43.75	2800	2.6564
1.352	46.88	3000	2.6720
1.3072	50.0	3200	2.6863
1.2773	53.12	3400	2.6931
1.2079	56.25	3600	2.7350
1.1768	59.38	3800	2.7521
1.1749	62.5	4000	2.7553
1.0857	65.62	4200	2.7921
1.0883	68.75	4400	2.7840
1.0307	71.88	4600	2.8110
1.0255	75.0	4800	2.8365
0.9992	78.12	5000	2.8358
0.9516	81.25	5200	2.8554
0.9363	84.38	5400	2.8742
0.91	87.5	5600	2.8923
0.895	90.62	5800	2.9057
0.8371	93.75	6000	2.9234
0.8588	96.88	6200	2.9443
0.8237	100.0	6400	2.9612
0.8147	103.12	6600	2.9633
0.7936	106.25	6800	2.9641
0.7883	109.38	7000	2.9711
0.7589	112.5	7200	2.9744
0.7277	115.62	7400	2.9879
0.7505	118.75	7600	2.9974
0.705	121.88	7800	3.0033
0.7111	125.0	8000	3.0032
0.7005	128.12	8200	3.0055
0.6961	131.25	8400	3.0168
0.6543	134.38	8600	3.0339
0.6482	137.5	8800	3.0312
0.6807	140.62	9000	3.0393
0.6365	143.75	9200	3.0413
0.648	146.88	9400	3.0461
0.6275	150.0	9600	3.0454
0.6284	153.12	9800	3.0552
0.6062	156.25	10000	3.0514
0.6312	159.38	10200	3.0487
0.6244	162.5	10400	3.0525
0.5792	165.62	10600	3.0547
0.5997	168.75	10800	3.0491
0.5972	171.88	11000	3.0542
0.5891	175.0	11200	3.0624
0.582	178.12	11400	3.0717
0.5934	181.25	11600	3.0683
0.5803	184.38	11800	3.0761
0.5724	187.5	12000	3.0777
0.6015	190.62	12200	3.0784
0.5874	193.75	12400	3.0792
0.5531	196.88	12600	3.0801
0.5863	200.0	12800	3.0801

Framework versions

Transformers 4.28.0
Pytorch 1.13.1+cu116
Datasets 2.12.0
Tokenizers 0.13.3

Library: transformers
Type: t5
License: apache-2.0
Created: May 23, 2023
Updated: Sep 1, 2023