text_shortening_model_v46

This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.8536
• Rouge1: 0.485
• Rouge2: 0.271
• Rougel: 0.4374
• Rougelsum: 0.4371
• Bert precision: 0.8676
• Bert recall: 0.8761
• Average word count: 9.1032
• Max word count: 17
• Min word count: 4
• Average token count: 15.8254
• % shortened texts with length > 12: 9.5238

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: 0.0003
• 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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bert precision	Bert recall	Average word count	Max word count	Min word count	Average token count	% shortened texts with length > 12
1.8213	1.0	42	2.1030	0.4561	0.2433	0.4131	0.4131	0.8606	0.8724	9.0529	15	5	14.7249	7.4074
0.8874	2.0	84	1.8034	0.4778	0.2609	0.4316	0.4316	0.8569	0.8787	10.6323	21	5	16.2963	19.3122
0.603	3.0	126	1.6613	0.4749	0.2594	0.425	0.4253	0.8576	0.8796	10.5106	21	5	16.2751	23.0159
0.5413	4.0	168	1.5975	0.4729	0.249	0.4258	0.4254	0.8635	0.8696	8.6481	16	4	14.3677	4.2328
0.3393	5.0	210	1.6755	0.4959	0.28	0.4476	0.4473	0.8687	0.8772	8.8942	20	5	15.8915	8.4656
0.2573	6.0	252	1.6908	0.4775	0.2589	0.4309	0.4307	0.866	0.873	8.9868	22	4	15.4339	10.3175
0.173	7.0	294	1.8536	0.485	0.271	0.4374	0.4371	0.8676	0.8761	9.1032	17	4	15.8254	9.5238

Framework versions

• Transformers 4.33.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3