metadata
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_km_10_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_km_10_v1_sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.7889908256880734
distilbert_km_10_v1_sst2
This model is a fine-tuned version of Hartunka/distilbert_km_10_v1 on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4489
- Accuracy: 0.7890
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 |
|---|---|---|---|---|
| 0.3957 | 1.0 | 264 | 0.4489 | 0.7890 |
| 0.2202 | 2.0 | 528 | 0.4966 | 0.8039 |
| 0.155 | 3.0 | 792 | 0.5528 | 0.7993 |
| 0.1106 | 4.0 | 1056 | 0.6223 | 0.8073 |
| 0.0808 | 5.0 | 1320 | 0.6404 | 0.8028 |
| 0.0613 | 6.0 | 1584 | 0.7933 | 0.8016 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1