Create README.md

README.md

@@ -0,0 +1,54 @@
+---
+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.