README.md

metadata

license: apache-2.0
base_model: facebook/bart-large
tags:
  - generated_from_trainer
metrics:
  - rouge
  - precision
  - recall
  - f1
model-index:
  - name: LLM_Teached_Bart_From_Scratch
    results: []

LLM_Teached_Bart_From_Scratch

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

• Loss: 1.5434
• Rouge1: 0.4476
• Rouge2: 0.2292
• Rougel: 0.3868
• Rougelsum: 0.3865
• Gen Len: 19.9007
• Precision: 0.9159
• Recall: 0.8916
• F1: 0.9034

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: 24
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• 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	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	Precision	Recall	F1
1.836	1.0	521	1.5560	0.4155	0.2028	0.3561	0.3559	19.9745	0.9105	0.8843	0.8971
1.5951	2.0	1042	1.5004	0.4333	0.2136	0.3695	0.3694	19.9353	0.9115	0.8886	0.8997
1.469	3.0	1563	1.4691	0.4355	0.2176	0.3729	0.3728	19.9385	0.912	0.8888	0.9001
1.373	4.0	2084	1.4658	0.4311	0.2164	0.3706	0.3704	19.9647	0.9137	0.8877	0.9003
1.2902	5.0	2605	1.4542	0.4368	0.2218	0.3762	0.376	19.9498	0.9136	0.8887	0.9008
1.222	6.0	3126	1.4584	0.4407	0.223	0.3802	0.3798	19.9425	0.914	0.8902	0.9018
1.1655	7.0	3647	1.4709	0.4404	0.2246	0.3806	0.3803	19.9327	0.9145	0.89	0.9019
1.11	8.0	4168	1.4724	0.4435	0.2269	0.383	0.3828	19.9084	0.9153	0.8906	0.9026
1.0629	9.0	4689	1.4853	0.4431	0.2273	0.3832	0.383	19.928	0.9155	0.8908	0.9028
1.023	10.0	5210	1.5033	0.4409	0.2247	0.3819	0.3818	19.944	0.9152	0.8897	0.9021
0.9862	11.0	5731	1.5074	0.4479	0.2278	0.3862	0.386	19.9124	0.9158	0.8916	0.9034
0.957	12.0	6252	1.5184	0.4461	0.2264	0.3846	0.3847	19.9033	0.9159	0.8909	0.903
0.9315	13.0	6773	1.5269	0.4473	0.2284	0.386	0.3858	19.9084	0.9156	0.8912	0.9031
0.9093	14.0	7294	1.5311	0.4453	0.2273	0.3846	0.3843	19.9135	0.9155	0.8909	0.9029
0.8927	15.0	7815	1.5351	0.4457	0.2267	0.3842	0.384	19.9065	0.9156	0.8909	0.9029
0.8773	16.0	8336	1.5434	0.4476	0.2292	0.3868	0.3865	19.9007	0.9159	0.8916	0.9034

Framework versions

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