transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_tiny_olda_book_5_v1 base_model:finetune:gokulsrinivasagan/bert_tiny_olda_book_5_v1 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_tiny_olda_book_5_v1
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert_tiny_olda_book_5_v1_mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.6838235294117647
      name: Accuracy
    - type: f1
      value: 0.8122270742358079
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert_tiny_olda_book_5_v1_mrpc

This model is a fine-tuned version of [gokulsrinivasagan/bert_tiny_olda_book_5_v1](https://huggingface.co/gokulsrinivasagan/bert_tiny_olda_book_5_v1) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6100
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6919        | 1.0   | 15   | 0.6360          | 0.6814   | 0.8099 | 0.7457         |
| 0.6351        | 2.0   | 30   | 0.6259          | 0.6838   | 0.8122 | 0.7480         |
| 0.6286        | 3.0   | 45   | 0.6206          | 0.6838   | 0.8122 | 0.7480         |
| 0.6307        | 4.0   | 60   | 0.6189          | 0.6838   | 0.8122 | 0.7480         |
| 0.628         | 5.0   | 75   | 0.6269          | 0.6838   | 0.8122 | 0.7480         |
| 0.6331        | 6.0   | 90   | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
| 0.6293        | 7.0   | 105  | 0.6206          | 0.6838   | 0.8122 | 0.7480         |
| 0.6312        | 8.0   | 120  | 0.6175          | 0.6838   | 0.8122 | 0.7480         |
| 0.6258        | 9.0   | 135  | 0.6159          | 0.6838   | 0.8122 | 0.7480         |
| 0.623         | 10.0  | 150  | 0.6129          | 0.6838   | 0.8122 | 0.7480         |
| 0.6163        | 11.0  | 165  | 0.6100          | 0.6838   | 0.8122 | 0.7480         |
| 0.6022        | 12.0  | 180  | 0.6165          | 0.6838   | 0.8122 | 0.7480         |
| 0.5892        | 13.0  | 195  | 0.6218          | 0.6789   | 0.8088 | 0.7438         |
| 0.5715        | 14.0  | 210  | 0.6281          | 0.6814   | 0.8105 | 0.7459         |
| 0.5497        | 15.0  | 225  | 0.6415          | 0.6789   | 0.8082 | 0.7436         |
| 0.5158        | 16.0  | 240  | 0.6702          | 0.6127   | 0.6938 | 0.6533         |


### Framework versions

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