| Name | nephased JSON |
| Version |
0.0.31
JSON |
| download |
| home_page | None |
| Summary | A BERT-based text sentiment classification pipeline for Nepali |
| upload_time | 2025-02-09 16:56:41 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
nlp
transformers
bert
nepali
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# nephased
> [!Warning]
> This section contains vulgar words
`Nephased` provides a BERT-based classification pipeline
for detecting Nepali text sentiment
## Installation
From TestPyPI:
```bash
pip install nephased
```
Or you can use the Nephased(finetune of distilbert-base-nepali) from [huggingface](https://huggingface.co/Vyke2000/Nephased)
## Usage
Import `Nephased` module using the following command.
```python
from nephased import Nephased
```
Initialize Nephased
```python
clf = Nephased()
```
- You can pass a single string:
```python
>>> clf.predict("थुक्क पैसा मा बिकने हीजडा")
'PROFANITY_0'
```
- or, a list of string:
```python
>>> clf.predict(["राडिको छोरोको शासन धेर दिन टिक्दैन |", "सुरु मा चाहिँ तैले यो देश छोडनु पर्यो |", "एसको घरमा आगो लाहिदे ।"])
['PROFANITY_1', 'GENERAL', 'VIOLENCE']
```
## About Output
Nephased can distinguish between 4 categories:
- GENERAL : Instance without any profanity or violence.
- PROFANITY_0 : Instance including rude, bad or slander which are not very harsh but offensive words used on day-to-day lives in Nepal.
- PROFANITY_1 : Instance including swear or curse words which are very harsh
- VIOLENCE : Instance including physical assualt or rape and pyromaniac act.
The guidelines for segragating such sentiments are on [NepsaGuidelines](https://github.com/oya163/nepali-sentiment-analysis/blob/master/guidelines/NepsaGuidelines_2020.pdf)
> [!NOTE]
> Nephased is trained on [NepSa](https://github.com/oya163/nepali-sentiment-analysis/blob/master/data/nepcls/csv/ss_ac_at_txt_unbal.csv) dataset \
> By default Nephased preprocesses the input:
>
> - stemming using [nepali-stemmer](https://github.com/oya163/nepali-stemmer)
> - lowering case, punctuation and stopwords removal \
> you can choose to not preprocess text when initializing Nephased
>
> ```python
> clf = nephased(preprocess_text = False)
> ```
Raw data
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"description": "# nephased\n\n> [!Warning]\n> This section contains vulgar words\n\n`Nephased` provides a BERT-based classification pipeline\nfor detecting Nepali text sentiment\n\n## Installation\n\nFrom TestPyPI:\n\n```bash\npip install nephased\n```\n\nOr you can use the Nephased(finetune of distilbert-base-nepali) from [huggingface](https://huggingface.co/Vyke2000/Nephased)\n\n## Usage\n\nImport `Nephased` module using the following command.\n\n```python\nfrom nephased import Nephased\n```\n\nInitialize Nephased\n\n```python\nclf = Nephased()\n```\n\n- You can pass a single string:\n\n```python\n>>> clf.predict(\"\u0925\u0941\u0915\u094d\u0915 \u092a\u0948\u0938\u093e \u092e\u093e \u092c\u093f\u0915\u0928\u0947 \u0939\u0940\u091c\u0921\u093e\")\n'PROFANITY_0'\n```\n\n- or, a list of string:\n\n```python\n>>> clf.predict([\"\u0930\u093e\u0921\u093f\u0915\u094b \u091b\u094b\u0930\u094b\u0915\u094b \u0936\u093e\u0938\u0928 \u0927\u0947\u0930 \u0926\u093f\u0928 \u091f\u093f\u0915\u094d\u0926\u0948\u0928 |\", \"\u0938\u0941\u0930\u0941 \u092e\u093e \u091a\u093e\u0939\u093f\u0901 \u0924\u0948\u0932\u0947 \u092f\u094b \u0926\u0947\u0936 \u091b\u094b\u0921\u0928\u0941 \u092a\u0930\u094d\u092f\u094b |\", \"\u090f\u0938\u0915\u094b \u0918\u0930\u092e\u093e \u0906\u0917\u094b \u0932\u093e\u0939\u093f\u0926\u0947 \u0964\"])\n['PROFANITY_1', 'GENERAL', 'VIOLENCE']\n```\n\n## About Output\n\nNephased can distinguish between 4 categories:\n\n- GENERAL : Instance without any profanity or violence.\n- PROFANITY_0 : Instance including rude, bad or slander which are not very harsh but offensive words used on day-to-day lives in Nepal.\n- PROFANITY_1 : Instance including swear or curse words which are very harsh\n- VIOLENCE : Instance including physical assualt or rape and pyromaniac act.\n\nThe guidelines for segragating such sentiments are on [NepsaGuidelines](https://github.com/oya163/nepali-sentiment-analysis/blob/master/guidelines/NepsaGuidelines_2020.pdf)\n\n> [!NOTE]\n> Nephased is trained on [NepSa](https://github.com/oya163/nepali-sentiment-analysis/blob/master/data/nepcls/csv/ss_ac_at_txt_unbal.csv) dataset \\\n> By default Nephased preprocesses the input:\n>\n> - stemming using [nepali-stemmer](https://github.com/oya163/nepali-stemmer)\n> - lowering case, punctuation and stopwords removal \\\n> you can choose to not preprocess text when initializing Nephased\n>\n> ```python\n> clf = nephased(preprocess_text = False)\n> ```\n",
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