---
library_name: transformers
language:
- en
base_model: gokulsrinivasagan/bert_base_lda_20_v1_book
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_lda_20_v1_book_qnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.862529745561047
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert_base_lda_20_v1_book_qnli

This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_20_v1_book](https://huggingface.co/gokulsrinivasagan/bert_base_lda_20_v1_book) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3267
- Accuracy: 0.8625

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4723        | 1.0   | 410  | 0.3705          | 0.8398   |
| 0.3383        | 2.0   | 820  | 0.3267          | 0.8625   |
| 0.2411        | 3.0   | 1230 | 0.3384          | 0.8629   |
| 0.1581        | 4.0   | 1640 | 0.4427          | 0.8477   |
| 0.1048        | 5.0   | 2050 | 0.5107          | 0.8514   |
| 0.0739        | 6.0   | 2460 | 0.5325          | 0.8574   |
| 0.0568        | 7.0   | 2870 | 0.6686          | 0.8451   |


### Framework versions

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