---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- gokulsrinivasagan/processed_wikitext-103-raw-v1-ld
metrics:
- accuracy
model-index:
- name: tinybert_base_train_kd
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: gokulsrinivasagan/processed_wikitext-103-raw-v1-ld
      type: gokulsrinivasagan/processed_wikitext-103-raw-v1-ld
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5510538509351868
---

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

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the gokulsrinivasagan/processed_wikitext-103-raw-v1-ld dataset.
It achieves the following results on the evaluation set:
- Loss: 62.6034
- Accuracy: 0.5511

## 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 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 447.3158      | 4.1982  | 10000 | 428.8420        | 0.1658   |
| 151.7919      | 8.3963  | 20000 | 135.7859        | 0.4816   |
| 95.9257       | 12.5945 | 30000 | 84.8065         | 0.5308   |
| 78.7736       | 16.7926 | 40000 | 70.8755         | 0.5468   |
| 70.9704       | 20.9908 | 50000 | 63.0908         | 0.5510   |


### Framework versions

- Transformers 4.51.2
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1