---
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_mnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MNLI
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8117371847030106
---

<!-- 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_mnli

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 MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4779
- Accuracy: 0.8117

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6384        | 1.0   | 1534  | 0.5400          | 0.7887   |
| 0.4424        | 2.0   | 3068  | 0.4842          | 0.8124   |
| 0.3282        | 3.0   | 4602  | 0.5306          | 0.8087   |
| 0.2371        | 4.0   | 6136  | 0.5695          | 0.8162   |
| 0.1756        | 5.0   | 7670  | 0.6396          | 0.8067   |
| 0.1332        | 6.0   | 9204  | 0.7668          | 0.8105   |
| 0.1049        | 7.0   | 10738 | 0.7943          | 0.8101   |


### Framework versions

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