README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - spearmanr
model-index:
  - name: bert-base-uncased-stsb
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE STSB
          type: glue
          config: stsb
          split: validation
          args: stsb
        metrics:
          - name: Spearmanr
            type: spearmanr
            value: 0.8872483552831365

bert-base-uncased-stsb

This model is a fine-tuned version of bert-base-uncased on the GLUE STSB dataset. It achieves the following results on the evaluation set:

• Loss: 0.4676
• Pearson: 0.8901
• Spearmanr: 0.8872
• Combined Score: 0.8887

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: 5e-05
• train_batch_size: 128
• eval_batch_size: 128
• seed: 10
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Pearson	Spearmanr	Combined Score
2.3939	1.0	45	0.7358	0.8686	0.8653	0.8669
0.5084	2.0	90	0.4959	0.8835	0.8799	0.8817
0.3332	3.0	135	0.5002	0.8846	0.8815	0.8830
0.2202	4.0	180	0.4962	0.8854	0.8827	0.8840
0.1642	5.0	225	0.4848	0.8864	0.8839	0.8852
0.1312	6.0	270	0.4987	0.8872	0.8866	0.8869
0.1057	7.0	315	0.4840	0.8895	0.8848	0.8871
0.0935	8.0	360	0.4753	0.8887	0.8840	0.8863
0.0835	9.0	405	0.4676	0.8901	0.8872	0.8887
0.0749	10.0	450	0.4808	0.8901	0.8867	0.8884
0.0625	11.0	495	0.4760	0.8893	0.8857	0.8875
0.0607	12.0	540	0.5113	0.8899	0.8859	0.8879
0.0564	13.0	585	0.4918	0.8900	0.8860	0.8880
0.0495	14.0	630	0.4749	0.8905	0.8868	0.8887
0.0446	15.0	675	0.4889	0.8888	0.8856	0.8872
0.045	16.0	720	0.4680	0.8918	0.8889	0.8904

Framework versions

• Transformers 4.26.0
• Pytorch 1.14.0a0+410ce96
• Datasets 2.9.0
• Tokenizers 0.13.2