README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: spellcorrector_3110_v17
    results: []

spellcorrector_3110_v17

This model is a fine-tuned version of google/canine-s on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0086
• Precision: 0.9991
• Recall: 0.9990
• F1: 0.9990
• Accuracy: 0.9977

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1377	1.0	1949	0.1114	0.9542	0.9820	0.9679	0.9757
0.1079	2.0	3898	0.0851	0.9680	0.9801	0.9740	0.9795
0.0904	3.0	5847	0.0717	0.9733	0.9842	0.9787	0.9823
0.0788	4.0	7796	0.0612	0.9773	0.9859	0.9816	0.9845
0.0709	5.0	9745	0.0548	0.9824	0.9843	0.9833	0.9858
0.0646	6.0	11694	0.0483	0.9847	0.9890	0.9868	0.9876
0.0579	7.0	13643	0.0426	0.9875	0.9889	0.9882	0.9889
0.0532	8.0	15592	0.0385	0.9897	0.9889	0.9893	0.9898
0.0477	9.0	17541	0.0320	0.9913	0.9932	0.9922	0.9916
0.044	10.0	19490	0.0268	0.9926	0.9952	0.9939	0.9929
0.0401	11.0	21439	0.0232	0.9937	0.9960	0.9949	0.9936
0.0366	12.0	23388	0.0200	0.9957	0.9961	0.9959	0.9944
0.0317	13.0	25337	0.0172	0.9968	0.9969	0.9968	0.9953
0.0294	14.0	27286	0.0146	0.9971	0.9979	0.9975	0.9959
0.0269	15.0	29235	0.0126	0.9979	0.9982	0.9981	0.9965
0.0248	16.0	31184	0.0119	0.9984	0.9982	0.9983	0.9968
0.0228	17.0	33133	0.0098	0.9987	0.9987	0.9987	0.9973
0.0203	18.0	35082	0.0091	0.9989	0.9987	0.9988	0.9975
0.0189	19.0	37031	0.0087	0.9990	0.9989	0.9990	0.9976
0.0198	20.0	38980	0.0086	0.9991	0.9990	0.9990	0.9977

Framework versions

• Transformers 4.28.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.13.3