Hugging Face's logo

alexeyak
/
deberta-v3-base-ner-B

Token Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
deberta-v3-base-ner-B / README.md
alexeyak's picture
alexeyak
Update README.md
0954a5a
preview code
|
raw
history blame contribute delete
3.76 kB
metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: deberta-v3-base-ner-B
    results: []

deberta-v3-base-ner-B

This model is a fine-tuned version of microsoft/deberta-v3-base on English part of Babelscape/multinerd dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0294
  • Precision: 0.9660
  • Recall: 0.9751
  • F1: 0.9705
  • Accuracy: 0.9929

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: 16
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0336 0.13 257 0.0345 0.9245 0.9386 0.9315 0.9885
0.0309 0.25 514 0.0296 0.9161 0.9624 0.9387 0.9892
0.0281 0.38 771 0.0251 0.9316 0.9539 0.9426 0.9908
0.0221 0.5 1028 0.0259 0.9381 0.9588 0.9483 0.9910
0.0234 0.63 1285 0.0260 0.9318 0.9640 0.9477 0.9904
0.0177 0.75 1542 0.0248 0.9331 0.9665 0.9495 0.9909
0.0213 0.88 1799 0.0228 0.9522 0.9593 0.9557 0.9918
0.0252 1.0 2056 0.0233 0.9517 0.9568 0.9542 0.9917
0.0143 1.13 2313 0.0256 0.9491 0.9641 0.9565 0.9918
0.0132 1.25 2570 0.0247 0.9536 0.9627 0.9581 0.9921
0.015 1.38 2827 0.0243 0.9467 0.9640 0.9553 0.9917
0.0148 1.5 3084 0.0254 0.9475 0.9677 0.9575 0.9918
0.0143 1.63 3341 0.0252 0.9491 0.9667 0.9578 0.9920
0.0112 1.75 3598 0.0244 0.9546 0.9626 0.9586 0.9923
0.0074 1.88 3855 0.0268 0.9490 0.9680 0.9584 0.9921
0.0068 2.0 4112 0.0257 0.9577 0.9610 0.9594 0.9923
0.0079 2.13 4369 0.0296 0.9457 0.9698 0.9576 0.9919
0.0067 2.26 4626 0.0290 0.9520 0.9686 0.9602 0.9922
0.0067 2.38 4883 0.0282 0.9553 0.9653 0.9603 0.9923
0.0044 2.51 5140 0.0303 0.9600 0.9622 0.9611 0.9926
0.005 2.63 5397 0.0318 0.9488 0.9703 0.9594 0.9920
0.006 2.76 5654 0.0295 0.9564 0.9663 0.9613 0.9925
0.0059 2.88 5911 0.0304 0.9586 0.9657 0.9621 0.9925

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0