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albert-base-v2-grammar-ner

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metadata
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: []

albert-base-v2-grammar-ner

This model is a fine-tuned version of 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