transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_tiny_olda_book_10_v1 base_model:finetune:gokulsrinivasagan/bert_tiny_olda_book_10_v1 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_tiny_olda_book_10_v1
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert_tiny_olda_book_10_v1_mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.7524509803921569
      name: Accuracy
    - type: f1
      value: 0.8346972176759411
      name: F1
---

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# bert_tiny_olda_book_10_v1_mrpc

This model is a fine-tuned version of [gokulsrinivasagan/bert_tiny_olda_book_10_v1](https://huggingface.co/gokulsrinivasagan/bert_tiny_olda_book_10_v1) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5350
- Accuracy: 0.7525
- F1: 0.8347
- Combined Score: 0.7936

## 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 OptimizerNames.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 | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6235        | 1.0   | 15   | 0.5878          | 0.6961   | 0.8069 | 0.7515         |
| 0.584         | 2.0   | 30   | 0.5651          | 0.7132   | 0.8152 | 0.7642         |
| 0.5365        | 3.0   | 45   | 0.5440          | 0.7230   | 0.8232 | 0.7731         |
| 0.4753        | 4.0   | 60   | 0.5350          | 0.7525   | 0.8347 | 0.7936         |
| 0.3882        | 5.0   | 75   | 0.5365          | 0.7353   | 0.8176 | 0.7764         |
| 0.2835        | 6.0   | 90   | 0.5699          | 0.7672   | 0.8365 | 0.8018         |
| 0.2543        | 7.0   | 105  | 0.8740          | 0.7426   | 0.8392 | 0.7909         |
| 0.2202        | 8.0   | 120  | 0.8167          | 0.7475   | 0.8418 | 0.7947         |
| 0.1664        | 9.0   | 135  | 0.7349          | 0.7721   | 0.8531 | 0.8126         |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.2.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
