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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_100_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_km_100_v1_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6495117982099268
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_km_100_v1_mnli
This model is a fine-tuned version of [Hartunka/bert_base_km_100_v1](https://huggingface.co/Hartunka/bert_base_km_100_v1) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8067
- Accuracy: 0.6495
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9905 | 1.0 | 1534 | 0.9129 | 0.5679 |
| 0.8746 | 2.0 | 3068 | 0.8564 | 0.6137 |
| 0.788 | 3.0 | 4602 | 0.8095 | 0.6408 |
| 0.7016 | 4.0 | 6136 | 0.8234 | 0.6445 |
| 0.6148 | 5.0 | 7670 | 0.8329 | 0.6544 |
| 0.5236 | 6.0 | 9204 | 0.9532 | 0.6469 |
| 0.4384 | 7.0 | 10738 | 0.9941 | 0.6431 |
| 0.3583 | 8.0 | 12272 | 1.1222 | 0.6404 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|