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--- |
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language: |
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- te |
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- en |
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tags: |
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- telugu |
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- NER |
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- TeluguNER |
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--- |
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## Direct Use |
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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. |
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## Downstream Use |
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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). |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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**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.** |
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```python |
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>>> from transformers import pipeline |
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>>> tokenizer = AutoTokenizer.from_pretrained("Pavan27/NER_Telugu_01") |
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>>> model = AutoModelForTokenClassification.from_pretrained("Pavan27/NER_Telugu_01") |
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>>> classifier = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities = True) |
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>>> classifier("వెస్టిండీస్పై పోర్ట్ ఆఫ్ స్పెయిన్ వేదిక జరుగుతున్న రెండో టెస్టు తొలి ఇన్నింగ్స్లో విరాట్ కోహ్లీ 121 పరుగులతో విదేశాల్లో సెంచరీ కరువును తీర్చుకున్నాడు.") |
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[{'entity_group': 'LOC', |
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'score': 0.9999062, |
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'word': 'వెస్టిండీస్', |
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'start': 0, |
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'end': 11}, |
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{'entity_group': 'LOC', |
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'score': 0.9998613, |
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'word': 'పోర్ట్ ఆఫ్ స్పెయిన్', |
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'start': 15, |
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'end': 34}, |
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{'entity_group': 'PER', |
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'score': 0.99996054, |
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'word': 'విరాట్ కోహ్లీ', |
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'start': 85, |
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'end': 98}] |
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``` |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. |
