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metadata
license: mit
base_model: xlnet-large-cased
tags:
  - generated_from_trainer
metrics:
  - f1
  - recall
  - precision
model-index:
  - name: task2_xlnet-large-cased_3_4_2e-05_0.01
    results: []

task2_xlnet-large-cased_3_4_2e-05_0.01

This model is a fine-tuned version of xlnet-large-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9482
  • F1: 0.7790
  • Recall: 0.7790
  • Precision: 0.7790

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: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss F1 Recall Precision
0.8074 1.0 745 0.7084 0.7574 0.7574 0.7574
0.7665 2.0 1490 0.7881 0.7628 0.7628 0.7628
0.6739 3.0 2235 0.9482 0.7790 0.7790 0.7790

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.3
  • Tokenizers 0.13.3