---
tags:
- generated_from_keras_callback
model-index:
- name: distilBERT-Nepali
  results:
  - task:
      type: Nepali-Language-Modelling
      name: Masked Language Modelling
    dataset:
      type: raygx/Nepali-Extended-Text-Corpus
      name: Nepali Language Corpus
    metrics:
    - type: PPL
      value: 17.31
      name: Perplexity
datasets:
- raygx/Nepali-Extended-Text-Corpus
- cc100
metrics:
- perplexity
language:
- ne
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# distilBERT-Nepali

This model fine-tuned model of raygx/distilBERT-Nepali, revision no.: b35360e0cffb71ae18aaf4ea00ff8369964243a2 

It achieves the following results on the evaluation set:

Perplexity:
>  - lowest: 17.31
>  - average: 19.12z

(This is because training is done in batches of data due to limited resources available)

Loss:
> - loss: 3.2503
> - val_loss: 3.0674

## Model description

This model is trained on [raygx/Nepali-Extended-Text-Corpus](https://huggingface.co/datasets/raygx/Nepali-Extended-Text-Corpus) dataset.
This dataset is a mixture of cc100 and [raygx/Nepali-Text-Corpus](https://huggingface.co/datasets/raygx/Nepali-Text-Corpus).
Thus this model is trained on 10 times more data than its previous self. 
Another change is, the tokenizer is different. Hence, it is a totally different model.

## Training procedure

Training is done by running one epoch at once on a batch of data. 
Thus, training is done for total 6 rounds.
So, there were total of 3 batches and 2 epochs.

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 16760, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

Perplexity:
  - lowest: 17.31
  - average: 19.12

Loss:
  - loss: 4.8605 - val_loss: 4.0510 - Perplexity: 56.96
  - loss: 3.8504 - val_loss: 3.5142 - Perplexity: 33.65
  - loss: 3.4918 - val_loss: 3.2408 - Perplexity: 25.64
  - loss: 3.2503 - val_loss: 3.0674 - Perplexity: 21.56
  - loss: 3.1324 - val_loss: 2.9243 - Perplexity: 18.49
  - loss: 3.2503 - val_loss: 3.0674 - Perplexity: 17.31

### Framework versions

- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3