---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: "पछि सडक निर्माण कार्य थप अनिश्चय तर्फ धकेलियो।"
  example_title: "Example-1"
- text: "यदी बैंक को शाखा ले ऋण दिन नमाने मा वा प्रक्रिया को बारेमा बुझाउन नचाहे को खण्ड मा उजुरी समेत गर्न सकिने"
  example_title: "Example-2"
model-index:
- name: distilbert-Nepali-NER
  results: []
---

<!-- 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. -->

# distilbert-Nepali-NER

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2917
- Precision: 0.0843
- Recall: 0.0538
- F1: 0.0657
- Accuracy: 0.9259

## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| No log        | 0.92  | 200  | 0.8685   | 0.0    | 0.4700          | 0.0       | 0.0    |
| No log        | 1.84  | 400  | 0.8984   | 0.0135 | 0.3581          | 0.0556    | 0.0077 |
| 0.4549        | 2.76  | 600  | 0.9087   | 0.0361 | 0.3188          | 0.0833    | 0.0231 |
| 0.4549        | 3.69  | 800  | 0.9111   | 0.0460 | 0.3040          | 0.0909    | 0.0308 |
| 0.2088        | 4.61  | 1000 | 0.9173   | 0.0396 | 0.2972          | 0.0556    | 0.0308 |
| 0.2088        | 5.53  | 1200 | 0.3065   | 0.0721 | 0.0615          | 0.0664    | 0.9100 |
| 0.2088        | 6.45  | 1400 | 0.2924   | 0.1724 | 0.0769          | 0.1064    | 0.9212 |
| 0.1601        | 7.37  | 1600 | 0.2929   | 0.0745 | 0.0538          | 0.0625    | 0.9234 |
| 0.1601        | 8.29  | 1800 | 0.2903   | 0.0893 | 0.0385          | 0.0538    | 0.9257 |
| 0.1114        | 9.22  | 2000 | 0.2917   | 0.0843 | 0.0538          | 0.0657    | 0.9259 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2