metadata
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_5_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_5_v2_qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6414058209774849
bert_base_rand_5_v2_qnli
This model is a fine-tuned version of Hartunka/bert_base_rand_5_v2 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6277
- Accuracy: 0.6414
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 |
|---|---|---|---|---|
| 0.6613 | 1.0 | 410 | 0.6415 | 0.6249 |
| 0.6213 | 2.0 | 820 | 0.6277 | 0.6414 |
| 0.5509 | 3.0 | 1230 | 0.6466 | 0.6365 |
| 0.4463 | 4.0 | 1640 | 0.7136 | 0.6511 |
| 0.3322 | 5.0 | 2050 | 0.7906 | 0.6625 |
| 0.2362 | 6.0 | 2460 | 0.9955 | 0.6469 |
| 0.1685 | 7.0 | 2870 | 1.0700 | 0.6546 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1