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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
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
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: 0.1134
- Accuracy: 0.9870
- F1 Macro: 0.7941
- F1 Micro: 0.9008
- Precision Macro: 0.8789
- Precision Micro: 0.9569
- Recall Macro: 0.7518
- Recall Micro: 0.8510
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:---------------:|:------------:|:------------:|
| 0.4297 | 1.0 | 93 | 0.2896 | 0.9313 | 0.1318 | 0.4462 | 0.1897 | 0.5163 | 0.1281 | 0.3928 |
| 0.2521 | 2.0 | 186 | 0.2192 | 0.9452 | 0.2315 | 0.5160 | 0.3282 | 0.6752 | 0.1962 | 0.4176 |
| 0.167 | 3.0 | 279 | 0.1630 | 0.9662 | 0.3546 | 0.7198 | 0.4142 | 0.8358 | 0.3295 | 0.6321 |
| 0.1026 | 4.0 | 372 | 0.1343 | 0.9733 | 0.4185 | 0.7769 | 0.5241 | 0.8732 | 0.3797 | 0.6998 |
| 0.0718 | 5.0 | 465 | 0.1231 | 0.9738 | 0.4644 | 0.7794 | 0.5584 | 0.8525 | 0.4382 | 0.7178 |
| 0.0483 | 6.0 | 558 | 0.1269 | 0.9778 | 0.4778 | 0.8204 | 0.6262 | 0.9415 | 0.4164 | 0.7269 |
| 0.0335 | 7.0 | 651 | 0.1162 | 0.9804 | 0.6028 | 0.8416 | 0.6985 | 0.8834 | 0.5846 | 0.8036 |
| 0.0233 | 8.0 | 744 | 0.1203 | 0.9813 | 0.5736 | 0.8475 | 0.7429 | 0.9496 | 0.4988 | 0.7652 |
| 0.0171 | 9.0 | 837 | 0.1052 | 0.9836 | 0.6502 | 0.8671 | 0.7023 | 0.8964 | 0.6490 | 0.8397 |
| 0.01 | 10.0 | 930 | 0.1125 | 0.9805 | 0.6681 | 0.8477 | 0.6854 | 0.8535 | 0.6875 | 0.8420 |
| 0.0084 | 11.0 | 1023 | 0.1058 | 0.9862 | 0.7195 | 0.8894 | 0.8004 | 0.9287 | 0.6870 | 0.8533 |
| 0.0051 | 12.0 | 1116 | 0.1092 | 0.9870 | 0.8015 | 0.9015 | 0.8810 | 0.95 | 0.7612 | 0.8578 |
| 0.0031 | 13.0 | 1209 | 0.1131 | 0.9865 | 0.8006 | 0.8983 | 0.8827 | 0.9429 | 0.7592 | 0.8578 |
| 0.0017 | 14.0 | 1302 | 0.1106 | 0.9873 | 0.8058 | 0.9039 | 0.8748 | 0.9525 | 0.7749 | 0.8600 |
| 0.0012 | 15.0 | 1395 | 0.1111 | 0.9875 | 0.7985 | 0.9058 | 0.8818 | 0.9596 | 0.7576 | 0.8578 |
| 0.0009 | 16.0 | 1488 | 0.1128 | 0.9870 | 0.7941 | 0.9008 | 0.8789 | 0.9569 | 0.7518 | 0.8510 |
| 0.0008 | 17.0 | 1581 | 0.1133 | 0.9870 | 0.7941 | 0.9008 | 0.8789 | 0.9569 | 0.7518 | 0.8510 |
| 0.0008 | 18.0 | 1674 | 0.1134 | 0.9870 | 0.7941 | 0.9008 | 0.8789 | 0.9569 | 0.7518 | 0.8510 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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