Edit model card

multibert_1310seed7

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: 0.4338
  • Precisions: 0.8841
  • Recall: 0.8144
  • F-measure: 0.8437
  • Accuracy: 0.9402

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: 7
  • 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
0.4441 1.0 236 0.2809 0.8700 0.7020 0.7222 0.9118
0.2161 2.0 472 0.2575 0.8741 0.7653 0.7818 0.9250
0.1277 3.0 708 0.2644 0.8331 0.8115 0.8175 0.9299
0.0891 4.0 944 0.2614 0.8671 0.8120 0.8341 0.9390
0.0559 5.0 1180 0.3259 0.8806 0.7923 0.8279 0.9332
0.0322 6.0 1416 0.3770 0.8807 0.8064 0.8333 0.9373
0.0241 7.0 1652 0.4548 0.8430 0.8213 0.8223 0.9323
0.0162 8.0 1888 0.3705 0.8493 0.8239 0.8343 0.9405
0.0099 9.0 2124 0.4498 0.8463 0.8094 0.8245 0.9369
0.0069 10.0 2360 0.4445 0.8606 0.8141 0.8328 0.9381
0.0062 11.0 2596 0.4429 0.8880 0.8075 0.8405 0.9383
0.0045 12.0 2832 0.4496 0.8794 0.8017 0.8322 0.9393
0.0041 13.0 3068 0.4338 0.8841 0.8144 0.8437 0.9402
0.0029 14.0 3304 0.4401 0.8850 0.8135 0.8437 0.9400

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
Downloads last month
2
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_1310seed7

Finetuned
(104)
this model