metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results_trainer
results: []
results_trainer
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4417
- Accuracy: 0.799
- F1: 0.7767
- Precision: 0.7296
- Recall: 0.8302
Model description
Test model created as part of an online course on adapters for working with text data.
Intended uses & limitations
This is just a test case for learning.
Training and evaluation data
HateEval 2019 - Task 5 data set
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 108
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4928 | 1.0 | 250 | 0.4792 | 0.768 | 0.7089 | 0.7513 | 0.6710 |
| 0.3599 | 2.0 | 500 | 0.4417 | 0.799 | 0.7767 | 0.7296 | 0.8302 |
| 0.346 | 3.0 | 750 | 0.4399 | 0.8065 | 0.7730 | 0.7636 | 0.7827 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1