metadata
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_50_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_rand_50_v2_qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6313380926231008
distilbert_rand_50_v2_qnli
This model is a fine-tuned version of Hartunka/distilbert_rand_50_v2 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6384
- Accuracy: 0.6313
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 |
|---|---|---|---|---|
| 0.6644 | 1.0 | 410 | 0.6444 | 0.6226 |
| 0.6252 | 2.0 | 820 | 0.6384 | 0.6313 |
| 0.56 | 3.0 | 1230 | 0.6712 | 0.6268 |
| 0.4515 | 4.0 | 1640 | 0.7168 | 0.6277 |
| 0.3311 | 5.0 | 2050 | 0.8598 | 0.6301 |
| 0.234 | 6.0 | 2460 | 1.1353 | 0.6215 |
| 0.1707 | 7.0 | 2870 | 1.2543 | 0.6149 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1