README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - esnli
metrics:
  - accuracy
  - f1
  - rouge
  - bleu
model-index:
  - name: google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: esnli
          type: esnli
          config: plain_text
          split: validation
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8622231253810201
          - name: F1
            type: f1
            value: 0.8623314280769628
          - name: Rouge1
            type: rouge
            value: 0.605873896307076
          - name: Bleu
            type: bleu
            value: 0.40472213589689604

google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48

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: 1.8720
• Accuracy: 0.8622
• F1: 0.8623
• Bertscore F1: 0.9329
• Rouge1: 0.6059
• Rouge2: 0.3988
• Rougel: 0.5475
• Rougelsum: 0.5496
• Bleu: 0.4047

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: 48
• eval_batch_size: 48
• 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
1.5084	0.17	2000	1.7484	0.8001	0.7997	0.9271	0.5768	0.3695	0.5209	0.5229	0.3703
1.2745	0.35	4000	1.8137	0.8113	0.8110	0.9304	0.5881	0.3804	0.5305	0.5325	0.3853
1.2287	0.52	6000	1.8358	0.8392	0.8403	0.9298	0.5828	0.3747	0.5282	0.5301	0.3778
1.1964	0.7	8000	1.8432	0.8430	0.8437	0.9326	0.5974	0.3905	0.5447	0.5462	0.3998
1.1674	0.87	10000	1.8567	0.8507	0.8485	0.9310	0.5947	0.3888	0.5383	0.5402	0.3892
1.1371	1.05	12000	1.8720	0.8622	0.8623	0.9329	0.6059	0.3988	0.5475	0.5496	0.4047

Framework versions

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