README.md

metadata

base_model: >-
  /exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_memsum_bp_5/checkpoint-1360
tags:
  - generated_from_trainer
datasets:
  - learn3r/gov_report_memsum_bp
metrics:
  - rouge
model-index:
  - name: longt5_xl_gov_memsum_bp_10
    results:
      - task:
          name: Summarization
          type: summarization
        dataset:
          name: learn3r/gov_report_memsum_bp
          type: learn3r/gov_report_memsum_bp
        metrics:
          - name: Rouge1
            type: rouge
            value: 67.1508

longt5_xl_gov_memsum_bp_10

This model is a fine-tuned version of /exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_memsum_bp_5/checkpoint-1360 on the learn3r/gov_report_memsum_bp dataset. It achieves the following results on the evaluation set:

• Loss: 1.1440
• Rouge1: 67.1508
• Rouge2: 38.9594
• Rougel: 36.6571
• Rougelsum: 64.7944
• Gen Len: 712.0165

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: 0.001
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 64
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.4356	1.0	272	1.1440	67.1508	38.9594	36.6571	64.7944	712.0165
0.3485	2.0	545	1.2622	66.9296	38.7595	36.4964	64.6309	808.4393
0.2933	3.0	818	1.3804	65.3911	38.0875	35.5935	63.1902	976.0340
0.2443	4.0	1091	1.4998	67.0929	38.0763	35.8704	64.9022	816.7253
0.2033	4.99	1360	1.5508	64.1251	36.9601	34.8068	61.9673	1052.8817

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1