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---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: albert-base-v2-grammar-ner-generic
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. -->
# albert-base-v2-grammar-ner-generic
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0991
- Accuracy: 0.9891
- F1 Macro: 0.9176
- F1 Micro: 0.9176
- Precision Macro: 0.9647
- Precision Micro: 0.9647
- Recall Macro: 0.875
- Recall Micro: 0.875
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 18
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro | Precision Macro | Precision Micro | Recall Macro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:---------------:|:------------:|:------------:|
| 0.244 | 1.0 | 93 | 0.1830 | 0.9305 | 0.2897 | 0.2897 | 0.3892 | 0.3892 | 0.2308 | 0.2308 |
| 0.1652 | 2.0 | 186 | 0.1476 | 0.9450 | 0.4236 | 0.4236 | 0.6644 | 0.6644 | 0.3109 | 0.3109 |
| 0.1119 | 3.0 | 279 | 0.1070 | 0.9656 | 0.6759 | 0.6759 | 0.7313 | 0.7313 | 0.6282 | 0.6282 |
| 0.0629 | 4.0 | 372 | 0.0884 | 0.9751 | 0.7764 | 0.7764 | 0.8453 | 0.8453 | 0.7179 | 0.7179 |
| 0.0408 | 5.0 | 465 | 0.0954 | 0.9748 | 0.7911 | 0.7911 | 0.7873 | 0.7873 | 0.7949 | 0.7949 |
| 0.028 | 6.0 | 558 | 0.0830 | 0.9799 | 0.8143 | 0.8143 | 0.9194 | 0.9194 | 0.7308 | 0.7308 |
| 0.0185 | 7.0 | 651 | 0.0756 | 0.9831 | 0.8700 | 0.8700 | 0.8714 | 0.8714 | 0.8686 | 0.8686 |
| 0.0125 | 8.0 | 744 | 0.0815 | 0.9841 | 0.8591 | 0.8591 | 0.9134 | 0.9134 | 0.8109 | 0.8109 |
| 0.0073 | 9.0 | 837 | 0.0809 | 0.9854 | 0.8773 | 0.8773 | 0.8963 | 0.8963 | 0.8590 | 0.8590 |
| 0.0046 | 10.0 | 930 | 0.0895 | 0.9870 | 0.8908 | 0.8908 | 0.9526 | 0.9526 | 0.8365 | 0.8365 |
| 0.0022 | 11.0 | 1023 | 0.0903 | 0.9867 | 0.8972 | 0.8972 | 0.9136 | 0.9136 | 0.8814 | 0.8814 |
| 0.0009 | 12.0 | 1116 | 0.0957 | 0.9889 | 0.9130 | 0.9130 | 0.9545 | 0.9545 | 0.875 | 0.875 |
| 0.0005 | 13.0 | 1209 | 0.0931 | 0.9891 | 0.9149 | 0.9149 | 0.9547 | 0.9547 | 0.8782 | 0.8782 |
| 0.0002 | 14.0 | 1302 | 0.0978 | 0.9891 | 0.9176 | 0.9176 | 0.9647 | 0.9647 | 0.875 | 0.875 |
| 0.0001 | 15.0 | 1395 | 0.0982 | 0.9891 | 0.9176 | 0.9176 | 0.9647 | 0.9647 | 0.875 | 0.875 |
| 0.0 | 16.0 | 1488 | 0.0986 | 0.9889 | 0.9161 | 0.9161 | 0.9613 | 0.9613 | 0.875 | 0.875 |
| 0.0 | 17.0 | 1581 | 0.0990 | 0.9891 | 0.9176 | 0.9176 | 0.9647 | 0.9647 | 0.875 | 0.875 |
| 0.0 | 18.0 | 1674 | 0.0991 | 0.9891 | 0.9176 | 0.9176 | 0.9647 | 0.9647 | 0.875 | 0.875 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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