metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sdg-bert-base-multilingual-cased-classification
results:
- task:
type: text-classification
name: text-classification
dataset:
name: albertmartinez/OSDG (2024-04-01)
type: albertmartinez/OSDG
split: test
metrics:
- type: accuracy
value: 0.7982568274259152
name: accuracy
args:
accuracy: 0.7982568274259152
total_time_in_seconds: 41.86629699298646
samples_per_second: 205.53525432262444
latency_in_seconds: 0.004865345379777625
sdg-bert-base-multilingual-cased-classification
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7135
- Accuracy: 0.7981
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use 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_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2927 | 1.0 | 269 | 0.8947 | 0.7515 |
| 0.7953 | 2.0 | 538 | 0.7700 | 0.7795 |
| 0.6549 | 3.0 | 807 | 0.7241 | 0.7937 |
| 0.5658 | 4.0 | 1076 | 0.7135 | 0.7984 |
| 0.4799 | 5.0 | 1345 | 0.7142 | 0.7941 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu118
- Datasets 2.19.2
- Tokenizers 0.21.0