README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - esnli
metrics:
  - f1
  - accuracy
base_model: roberta-base
model-index:
  - name: roberta-base-e-snli-classification-nli-base
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: esnli
          type: esnli
          config: plain_text
          split: validation
          args: plain_text
        metrics:
          - type: f1
            value: 0.9108298866502319
            name: F1
          - type: accuracy
            value: 0.9109937004673847
            name: Accuracy

roberta-base-e-snli-classification-nli-base

This model is a fine-tuned version of roberta-base on the esnli dataset. It achieves the following results on the evaluation set:

• Loss: 0.2611
• F1: 0.9108
• Accuracy: 0.9110

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: 1e-05
• 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: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
1.0317	0.05	400	0.5734	0.7771	0.7803
0.544	0.09	800	0.3994	0.8548	0.8555
0.4604	0.14	1200	0.3492	0.8681	0.8687
0.4235	0.19	1600	0.3323	0.8764	0.8777
0.3934	0.23	2000	0.3225	0.8831	0.8841
0.3863	0.28	2400	0.3086	0.8875	0.8872
0.3767	0.33	2800	0.2972	0.8892	0.8898
0.3726	0.37	3200	0.2910	0.8932	0.8936
0.3624	0.42	3600	0.2934	0.8934	0.8937
0.361	0.47	4000	0.2831	0.8989	0.8989
0.3553	0.51	4400	0.2905	0.8985	0.8993
0.3451	0.56	4800	0.2725	0.9019	0.9024
0.3475	0.61	5200	0.2712	0.9046	0.9051
0.3398	0.65	5600	0.2787	0.9024	0.9028
0.3322	0.7	6000	0.2697	0.9043	0.9046
0.3288	0.75	6400	0.2722	0.9006	0.9013
0.324	0.79	6800	0.2677	0.9066	0.9066
0.3335	0.84	7200	0.2629	0.9075	0.9077
0.3309	0.89	7600	0.2577	0.9058	0.9061
0.3236	0.93	8000	0.2561	0.9121	0.9121
0.3183	0.98	8400	0.2556	0.9084	0.9088
0.3022	1.03	8800	0.2668	0.9056	0.9064
0.2974	1.07	9200	0.2519	0.9087	0.9092
0.29	1.12	9600	0.2554	0.9103	0.9109
0.2855	1.16	10000	0.2611	0.9108	0.9110

Framework versions

• Transformers 4.27.1
• Pytorch 1.12.1+cu113
• Datasets 2.10.1
• Tokenizers 0.13.2