metadata
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_100_v2_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
bert_base_rand_100_v2_wnli
This model is a fine-tuned version of Hartunka/bert_base_rand_100_v2 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6978
- Accuracy: 0.5634
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- 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
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7033 | 1.0 | 3 | 0.6978 | 0.5634 |
| 0.6943 | 2.0 | 6 | 0.7144 | 0.3944 |
| 0.6942 | 3.0 | 9 | 0.7126 | 0.2817 |
| 0.6971 | 4.0 | 12 | 0.7131 | 0.5352 |
| 0.693 | 5.0 | 15 | 0.7353 | 0.1972 |
| 0.693 | 6.0 | 18 | 0.7650 | 0.2535 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1