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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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+
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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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+
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+ ## Downstream Use
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+
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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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+
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+ ## Out-of-Scope Use
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+
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+ The model should not be used to intentionally create hostile or alienating environments for people.
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+
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+ # Bias, Risks, and Limitations
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+
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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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+
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+
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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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+
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+
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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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+
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+ ## Recommendations
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.