---
language:
- te
- en
tags:
- telugu
- NER
- TeluguNER
---
## Direct Use

The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. 

## Downstream Use

Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face [token classification docs](https://huggingface.co/tasks/token-classification).

## Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people. 

# Bias, Risks, and Limitations

**CONTENT WARNING: Readers should be made aware that language generated by this model may be disturbing or offensive to some and may propagate historical and current stereotypes.**


```python
>>> from transformers import pipeline
>>> tokenizer = AutoTokenizer.from_pretrained("Pavan27/NER_Telugu_01")
>>> model = AutoModelForTokenClassification.from_pretrained("Pavan27/NER_Telugu_01")
>>> classifier = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities = True)
>>> classifier("వెస్టిండీస్‌పై పోర్ట్ ఆఫ్ స్పెయిన్‌ వేదిక జరుగుతున్న రెండో టెస్టు తొలి ఇన్నింగ్స్‌లో విరాట్ కోహ్లీ 121 పరుగులతో విదేశాల్లో సెంచరీ కరువును తీర్చుకున్నాడు.")


[{'entity_group': 'LOC',
  'score': 0.9999062,
  'word': 'వెస్టిండీస్',
  'start': 0,
  'end': 11},
 {'entity_group': 'LOC',
  'score': 0.9998613,
  'word': 'పోర్ట్ ఆఫ్ స్పెయిన్',
  'start': 15,
  'end': 34},
 {'entity_group': 'PER',
  'score': 0.99996054,
  'word': 'విరాట్ కోహ్లీ',
  'start': 85,
  'end': 98}]
```

## Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.