|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: spellcorrector_3110_v17 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# spellcorrector_3110_v17 |
|
|
|
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/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 |
|
|
