Hugging Face's logo

SriDoc
/
LiLT-SER

Token Classification
Transformers
Safetensors
lilt
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
LiLT-SER / README.md
kavg's picture
kavg
End of training
479c5a6 verified
preview code
|
raw
history blame contribute delete
3.81 kB
metadata
license: mit
base_model: kavg/LiLT-SER-FR
tags:
  - generated_from_trainer
datasets:
  - xfun
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LiLT-SER-FR-SIN
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xfun
          type: xfun
          config: xfun.sin
          split: validation
          args: xfun.sin
        metrics:
          - name: Precision
            type: precision
            value: 0.7617924528301887
          - name: Recall
            type: recall
            value: 0.7955665024630542
          - name: F1
            type: f1
            value: 0.7783132530120481
          - name: Accuracy
            type: accuracy
            value: 0.8647776686772338

LiLT-SER-FR-SIN

This model is a fine-tuned version of kavg/LiLT-SER-FR on the xfun dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2426
  • Precision: 0.7618
  • Recall: 0.7956
  • F1: 0.7783
  • Accuracy: 0.8648

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: 8
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0057 21.74 500 0.8019 0.6884 0.7020 0.6951 0.8582
0.008 43.48 1000 1.0139 0.6963 0.7623 0.7278 0.8648
0.0006 65.22 1500 0.9878 0.7090 0.7562 0.7318 0.8592
0.0038 86.96 2000 1.2269 0.7104 0.7401 0.7250 0.8373
0.001 108.7 2500 0.9751 0.7276 0.7697 0.7481 0.8707
0.0004 130.43 3000 1.0918 0.7479 0.7672 0.7574 0.8538
0.0003 152.17 3500 1.0782 0.7102 0.7635 0.7359 0.8604
0.0 173.91 4000 1.0515 0.7402 0.7894 0.7640 0.8704
0.0001 195.65 4500 1.2154 0.7373 0.7709 0.7538 0.8419
0.0 217.39 5000 1.1026 0.7411 0.7722 0.7563 0.8642
0.0001 239.13 5500 1.0594 0.7262 0.7512 0.7385 0.8576
0.0 260.87 6000 1.1103 0.7377 0.7759 0.7563 0.8609
0.0 282.61 6500 1.1591 0.7267 0.7599 0.7429 0.8610
0.0 304.35 7000 1.2382 0.7574 0.7537 0.7556 0.8562
0.0 326.09 7500 1.2027 0.7485 0.7882 0.7678 0.8578
0.0001 347.83 8000 1.1492 0.7433 0.7808 0.7616 0.8659
0.0002 369.57 8500 1.1924 0.7570 0.7980 0.7770 0.8655
0.0 391.3 9000 1.2426 0.7618 0.7956 0.7783 0.8648
0.0 413.04 9500 1.3078 0.7620 0.7808 0.7713 0.8597
0.0 434.78 10000 1.3219 0.7639 0.7771 0.7705 0.8579

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.1