Edit model card

multibert_1210seed24

This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6397
  • Precisions: 0.8875
  • Recall: 0.7915
  • F-measure: 0.8255
  • Accuracy: 0.9112

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: 7.5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 24
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 14

Training results

Training Loss Epoch Step Validation Loss Precisions Recall F-measure Accuracy
0.5949 1.0 236 0.4396 0.8425 0.6484 0.6768 0.8569
0.3352 2.0 472 0.4132 0.7836 0.7344 0.7453 0.8862
0.2148 3.0 708 0.3528 0.8396 0.7759 0.8020 0.8985
0.1389 4.0 944 0.4093 0.8386 0.7431 0.7775 0.8931
0.099 5.0 1180 0.4169 0.8501 0.7998 0.8200 0.9022
0.078 6.0 1416 0.4629 0.7912 0.7756 0.7815 0.8900
0.0536 7.0 1652 0.4658 0.8394 0.8096 0.8235 0.9098
0.0316 8.0 1888 0.5609 0.8440 0.7790 0.8044 0.9019
0.0217 9.0 2124 0.5870 0.8686 0.7814 0.8128 0.9055
0.0126 10.0 2360 0.5636 0.8613 0.7997 0.8255 0.9059
0.0115 11.0 2596 0.5978 0.8721 0.7964 0.8232 0.9093
0.0082 12.0 2832 0.6072 0.8645 0.7904 0.8184 0.9098
0.0042 13.0 3068 0.6332 0.8801 0.7903 0.8230 0.9104
0.0033 14.0 3304 0.6397 0.8875 0.7915 0.8255 0.9112

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
Downloads last month
8
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for Tommert25/multibert_1210seed24

Finetuned
(104)
this model