metadata
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_km_20_v2_rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5234657039711191
bert_base_km_20_v2_rte
This model is a fine-tuned version of Hartunka/bert_base_km_20_v2 on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.6976
- Accuracy: 0.5235
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 |
|---|---|---|---|---|
| 0.7038 | 1.0 | 10 | 0.6976 | 0.5235 |
| 0.6567 | 2.0 | 20 | 0.7068 | 0.5235 |
| 0.6219 | 3.0 | 30 | 0.7116 | 0.5235 |
| 0.5515 | 4.0 | 40 | 0.7494 | 0.5271 |
| 0.4533 | 5.0 | 50 | 0.8515 | 0.5271 |
| 0.3589 | 6.0 | 60 | 1.0374 | 0.4946 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1