---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- gokulsrinivasagan/processed_wikitext-103-raw-v1-ld-5
metrics:
- accuracy
model-index:
- name: bert_base_lda_5_v1
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: gokulsrinivasagan/processed_wikitext-103-raw-v1-ld-5
      type: gokulsrinivasagan/processed_wikitext-103-raw-v1-ld-5
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.15604339718596372
---

<!-- 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_base_lda_5_v1

This model is a fine-tuned version of [](https://huggingface.co/) on the gokulsrinivasagan/processed_wikitext-103-raw-v1-ld-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 7.2047
- Accuracy: 0.1560

## 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: 0.0001
- train_batch_size: 96
- eval_batch_size: 96
- 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
- lr_scheduler_warmup_steps: 10000
- num_epochs: 25

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 7.5336        | 4.1982  | 10000 | 7.4824          | 0.1509   |
| 7.3176        | 8.3963  | 20000 | 7.3286          | 0.1524   |
| 7.2331        | 12.5945 | 30000 | 7.2804          | 0.1554   |
| 7.1765        | 16.7926 | 40000 | 7.2542          | 0.1544   |
| 7.1457        | 20.9908 | 50000 | 7.2485          | 0.1525   |


### Framework versions

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