README.md

---
license: mit
tags:
- nepali-nlp, nepali-news-classificiation, nlp, transformers
model-index:
- name: patrakar
  results: []
widget:

- text: "नेकपा (एमाले)का नेता गोकर्णराज विष्टले सहमति र सहकार्यबाटै संविधान बनाउने तथा जनताको जीवनस्तर उकास्ने काम गर्नु नै अबको मुख्य काम रहेको बताएका छन् ।"
  example_title: "Example 1"
- text: "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"
  example_title: "Example 2"
- text: "छाउगोठ भत्काइदिए फेरि बनाउने, बनाउन नपाए ओडार वा बारीका कान्लामा रात बिताउने र ज्यानकै जोखिम मोल्न तयार हुने प्रवृत्तिबाट थाहा हुन्छ– छाउपडी प्रथा हटाउनका लागि बनाइएका अहिलेसम्मका योजना, रणनीति उपयुक्त छैनन् र गरिएको लगानी खेर गइरहेको छ"
  example_title: "Example 3"

---

# patrakar/ पत्रकार (Nepali News Classifier)

Last updated: September 2022


DistilBERT model with  on 9 newsgroup datasets for the Nepali language with 95.475% accuracy.
## Model Details

patrakar is a DistilBERT pre-trained sequence classification transformer model which classifies Nepali language news into 9 newsgroup category, such as:

- politics
- opinion
- bank
- entertainment
- economy
- health
- literature
- sports
- tourism

It is developed by Sahaj Raj Malla  to be generally usefuly for general public and so that others could explore them for commercial and scientific purposes. This model was trained on [Sakonii/distilgpt2-nepali](https://huggingface.co/Sakonii/distilgpt2-nepali) model.

It achieves the following results on the test dataset:

| Total Number of samples | Accuracy(%)
|:-------------:|:---------------:
| 5670        | 95.475         

### Model date
September 2022

### Model type
Sequence classification model

### Model version
1.0.0

## Model Usage
This model can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:

```python

from transformers import pipeline, set_seed

set_seed(42)

model_name = "sahajrajmalla/patrakar"
classifier = pipeline('text-classification', model=model_name)

text = "नेकपा (एमाले)का नेता गोकर्णराज विष्टले सहमति र सहकार्यबाटै संविधान बनाउने तथा जनताको जीवनस्तर उकास्ने काम गर्नु नै अबको मुख्य काम रहेको बताएका छन् ।"

classifier(text)
```


Here is how we can use the model to get the features of a given text in PyTorch:
```python
!pip install transformers pytorch

from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification

import torch
import torch.nn.functional as F



# initializing model and tokenizer
model_name = "sahajrajmalla/patrakar"

# downloading tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# downloading model
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def tokenize_function(examples):
    return tokenizer(examples["data"], padding="max_length", truncation=True)


# predicting with the model
word_i_want_to_predict = "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"

# initializing our labels
label_list = [
                "bank",
                "economy",
                "entertainment",
                "health",
                "literature",
                "opinion",
                "politics",
                "sports",
                "tourism"
]

batch = tokenizer(word_i_want_to_predict, padding=True, truncation=True, max_length=512, return_tensors='pt')

with torch.no_grad():
    outputs = model(**batch)
    predictions = F.softmax(outputs.logits, dim=1)
    labels = torch.argmax(predictions, dim=1)

print(f"The sequence: \n\n {word_i_want_to_predict} \n\n is predicted to be of newsgroup {label_list[labels.item()]}")
```

## Training data
This model is trained on 50,945 rows of Nepali language news grouped [dataset](https://www.kaggle.com/competitions/text-it-meet-22/data?select=train.csv) found on Kaggle which was also used in IT Meet 2022 Text challenge.

## 


## Framework versions
- Transformers 4.20.1
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.11.6