transformers tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:glue base_model:gokulsrinivasagan/bert_tiny_lda_100_v1 base_model:finetune:gokulsrinivasagan/bert_tiny_lda_100_v1 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokulsrinivasagan/bert_tiny_lda_100_v1
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert_tiny_lda_100_v1_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8543408360128617
      name: Accuracy
    - type: f1
      value: 0.8063020096700984
      name: F1
---

<!-- 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_tiny_lda_100_v1_qqp

This model is a fine-tuned version of [gokulsrinivasagan/bert_tiny_lda_100_v1](https://huggingface.co/gokulsrinivasagan/bert_tiny_lda_100_v1) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3551
- Accuracy: 0.8543
- F1: 0.8063
- Combined Score: 0.8303

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4874        | 1.0   | 1422  | 0.4274          | 0.7980   | 0.7125 | 0.7553         |
| 0.388         | 2.0   | 2844  | 0.3786          | 0.8224   | 0.7726 | 0.7975         |
| 0.3354        | 3.0   | 4266  | 0.3613          | 0.8372   | 0.7899 | 0.8136         |
| 0.2928        | 4.0   | 5688  | 0.3564          | 0.8447   | 0.7830 | 0.8139         |
| 0.2583        | 5.0   | 7110  | 0.3614          | 0.8509   | 0.7997 | 0.8253         |
| 0.2277        | 6.0   | 8532  | 0.3551          | 0.8543   | 0.8063 | 0.8303         |
| 0.2014        | 7.0   | 9954  | 0.3854          | 0.8552   | 0.8093 | 0.8322         |
| 0.1784        | 8.0   | 11376 | 0.3979          | 0.8545   | 0.8064 | 0.8305         |
| 0.1578        | 9.0   | 12798 | 0.4261          | 0.8558   | 0.8102 | 0.8330         |
| 0.1403        | 10.0  | 14220 | 0.4443          | 0.8588   | 0.8108 | 0.8348         |
| 0.1246        | 11.0  | 15642 | 0.4678          | 0.8567   | 0.8093 | 0.8330         |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
