Edit model card

gbert-large_ner

This model is a fine-tuned version of deepset/gbert-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3755
  • Precision: 0.9010
  • Recall: 0.8948
  • F1: 0.8975
  • Accuracy: 0.9521

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 438 0.2334 0.8727 0.8653 0.8649 0.9303
0.3598 2.0 876 0.2149 0.8885 0.8649 0.8757 0.9391
0.1678 3.0 1314 0.2257 0.8820 0.8906 0.8847 0.9461
0.1054 4.0 1752 0.2580 0.8902 0.8884 0.8884 0.9463
0.0645 5.0 2190 0.2881 0.8896 0.8820 0.8833 0.9451
0.0436 6.0 2628 0.2767 0.8922 0.8911 0.8914 0.9479
0.0245 7.0 3066 0.3190 0.9026 0.9038 0.9030 0.9534
0.0108 8.0 3504 0.3547 0.8879 0.8886 0.8876 0.9474
0.0108 9.0 3942 0.3780 0.8943 0.8886 0.8910 0.9494
0.0074 10.0 4380 0.3755 0.9010 0.8948 0.8975 0.9521

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
Downloads last month
4
Safetensors
Model size
335M params
Tensor type
F32
·
Inference API
Unable to determine this model's library. Check the docs .

Model tree for izaitova/gbert-large_ner

Finetuned
(13)
this model