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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_5_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_5_v1_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6433075671277462
---
<!-- 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_rand_5_v1_mnli
This model is a fine-tuned version of [Hartunka/bert_base_rand_5_v1](https://huggingface.co/Hartunka/bert_base_rand_5_v1) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8189
- Accuracy: 0.6433
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9872 | 1.0 | 1534 | 0.9292 | 0.5555 |
| 0.891 | 2.0 | 3068 | 0.8749 | 0.5992 |
| 0.8134 | 3.0 | 4602 | 0.8478 | 0.6162 |
| 0.7398 | 4.0 | 6136 | 0.8198 | 0.6414 |
| 0.6672 | 5.0 | 7670 | 0.8427 | 0.6495 |
| 0.5962 | 6.0 | 9204 | 0.8499 | 0.6586 |
| 0.5244 | 7.0 | 10738 | 0.8884 | 0.6546 |
| 0.456 | 8.0 | 12272 | 0.9954 | 0.6505 |
| 0.3923 | 9.0 | 13806 | 1.0534 | 0.6501 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|