README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - esnli
metrics:
  - accuracy
  - f1
  - rouge
  - bleu
base_model: google/flan-t5-small
model-index:
  - name: google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b64
    results:
      - task:
          type: text2text-generation
          name: Sequence-to-sequence Language Modeling
        dataset:
          name: esnli
          type: esnli
          config: plain_text
          split: validation
          args: plain_text
        metrics:
          - type: accuracy
            value: 0.8691322901849218
            name: Accuracy
          - type: f1
            value: 0.8686267742768865
            name: F1
          - type: rouge
            value: 0.6062872493545299
            name: Rouge1
          - type: bleu
            value: 0.4012059786299585
            name: Bleu

google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b64

This model is a fine-tuned version of google/flan-t5-small on the esnli dataset. It achieves the following results on the evaluation set:

• Loss: 1.8703
• Accuracy: 0.8691
• F1: 0.8686
• Bertscore F1: 0.9338
• Rouge1: 0.6063
• Rouge2: 0.3995
• Rougel: 0.5500
• Rougelsum: 0.5521
• Bleu: 0.4012

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Bertscore F1	Rouge1	Rouge2	Rougel	Rougelsum	Bleu
1.4692	0.23	2000	1.7872	0.8212	0.8203	0.9287	0.5787	0.3685	0.5239	0.5257	0.3856
1.2505	0.47	4000	1.8808	0.8263	0.8264	0.9308	0.5870	0.3749	0.5321	0.5337	0.3904
1.2003	0.7	6000	1.8477	0.8475	0.8481	0.9325	0.5984	0.3913	0.5452	0.5469	0.4004
1.1624	0.93	8000	1.8244	0.8599	0.8587	0.9335	0.6029	0.3928	0.5441	0.5457	0.4024
1.1155	1.16	10000	1.8499	0.8695	0.8688	0.9331	0.6083	0.4019	0.5519	0.5540	0.4022
1.0913	1.4	12000	1.8703	0.8691	0.8686	0.9338	0.6063	0.3995	0.5500	0.5521	0.4012

Framework versions

• Transformers 4.27.4
• Pytorch 2.0.0+cu117
• Datasets 2.11.0
• Tokenizers 0.13.2