README.md

metadata

base_model: google/pegasus-large
tags:
  - generated_from_trainer
metrics:
  - rouge
  - precision
  - recall
  - f1
model-index:
  - name: LLM_Teached_Pegasus_100k_FS
    results: []

LLM_Teached_Pegasus_100k_FS

This model is a fine-tuned version of google/pegasus-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.4469
• Rouge1: 0.4939
• Rouge2: 0.2453
• Rougel: 0.4133
• Rougelsum: 0.4134
• Gen Len: 25.9629
• Precision: 0.9133
• Recall: 0.9138
• F1: 0.9134

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: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 6
• total_train_batch_size: 96
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 16
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	F1	Gen Len	Validation Loss	Precision	Recall	Rouge1	Rouge2	Rougel	Rougelsum
1.781	2.0	1388	0.9088	26.8891	1.5797	0.908	0.91	0.4708	0.2219	0.3892	0.389
1.6618	3.0	2083	0.91	26.7282	1.5411	0.9094	0.9111	0.4776	0.2303	0.3977	0.3973
1.626	4.0	2776	0.911	26.7596	1.5171	0.9102	0.9121	0.4834	0.2345	0.402	0.402
1.5918	5.0	3471	0.9112	26.6476	1.5001	0.9106	0.9122	0.4853	0.2365	0.4045	0.4045
1.5586	6.0	4164	0.9116	26.7778	1.4880	0.9108	0.9127	0.4875	0.2373	0.4063	0.4063
1.5375	7.0	4858	1.4768	0.4898	0.24	0.4083	0.4083	26.3991	0.9116	0.9128	0.912
1.5146	8.0	5553	1.4686	0.4907	0.241	0.4088	0.4089	26.156	0.9123	0.9133	0.9126
1.5006	9.0	6247	1.4636	0.4914	0.2419	0.4097	0.4099	26.2629	0.9122	0.9135	0.9127
1.49	10.0	6942	1.4580	0.4911	0.2429	0.4109	0.411	26.0273	0.9125	0.9133	0.9127
1.4749	11.0	7636	1.4546	0.4932	0.244	0.4121	0.4123	26.2304	0.9127	0.9138	0.9131
1.4661	12.0	8331	1.4514	0.4937	0.2448	0.4126	0.4127	25.8778	0.9133	0.9136	0.9132
1.4575	13.0	9025	1.4499	0.4947	0.2453	0.4139	0.414	26.1151	0.913	0.914	0.9133
1.4511	14.0	9720	1.4478	0.4939	0.2451	0.4133	0.4134	26.0287	0.9131	0.9138	0.9133
1.4519	15.0	10414	1.4471	0.4938	0.2451	0.4134	0.4134	25.9078	0.9132	0.9137	0.9133
1.4439	15.99	11104	1.4469	0.4939	0.2453	0.4133	0.4134	25.9629	0.9133	0.9138	0.9134

Framework versions

• Transformers 4.36.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.15.0