Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- eriktks/conll2003
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- distilbert/distilbert-base-cased
+---
+
+# DistilBERT Base Cased Fine-Tuned on CoNLL2003 for English Named Entity Recognition (NER)
+
+This model is a fine-tuned version of [DistilBERT-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the [CoNLL2003](https://huggingface.co/datasets/eriktks/conll2003) dataset for Named Entity Recognition (NER) in English. The CoNLL2003 dataset contains four types of named entities: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC).
+
+## Model Details
+- Model Architecture: BERT (Bidirectional Encoder Representations from Transformers)
+- Pre-trained Base Model: bert-base-cased
+- Dataset: CoNLL2003 (NER task)
+- Languages: English
+- Fine-tuned for: Named Entity Recognition (NER)
+- Entities recognized:
+- PER: Person
+- LOC: Location
+- ORG: Organization
+- MISC: Miscellaneous entities
+
+## Use Cases
+This model is ideal for tasks that require identifying and classifying named entities within English text, such as:
+
+- Information extraction from unstructured text
+- Content classification and tagging
+- Automated text summarization
+- Question answering systems with a focus on entity recognition
+
+## How to Use
+To use this model in your code, you can load it via Hugging Face’s Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("MrRobson9/distilbert-base-cased-finetuned-conll2003-english-ner")
+model = AutoModelForTokenClassification.from_pretrained("MrRobson9/distilbert-base-cased-finetuned-conll2003-english-ner")
+
+nlp_ner = pipeline("ner", model=model, tokenizer=tokenizer)
+result = nlp_ner("John lives in New York and works for the United Nations.")
+print(result)
+```
+
+## Performance
+|accuracy |precision |recall |f1-score|
+|:-------:|:--------:|:-----:|:------:|
+| 0.987   | 0.937    | 0.941 | 0.939  |
+
+## License
+This model is licensed under the same terms as the BERT-base-cased model and the CoNLL2003 dataset. Please ensure compliance with all respective licenses when using this model.