Update README.md

README.md

@@ -15,11 +15,8 @@ language:
 - en
 pipeline_tag: token-classification
 ---
-# distilbert-NER
 
-If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks!
-
-<a href="https://www.buymeacoffee.com/dslim" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/arial-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
+## 
 
 ## Model description
 
@@ -29,13 +26,6 @@ This model accurately identifies the same four types of entities as its BERT cou
 
 The model was fine-tuned on the English version of the [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset, which is widely recognized for its comprehensive and diverse range of entity types.
 
-### Available NER models 
-| Model Name | Description | Parameters |
-|-------------------|-------------|------------------|
-| [distilbert-NER](https://huggingface.co/dslim/distilbert-NER) | Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT | 66M |
-| [bert-large-NER](https://huggingface.co/dslim/bert-large-NER/) | Fine-tuned bert-large-cased - larger model with slightly better performance | 340M |
-| [bert-base-NER](https://huggingface.co/dslim/bert-base-NER)-([uncased](https://huggingface.co/dslim/bert-base-NER-uncased)) | Fine-tuned bert-base, available in both cased and uncased versions | 110M |
-
 ## Intended uses & limitations
 
 #### How to use
@@ -46,8 +36,8 @@ This model can be utilized with the Transformers *pipeline* for NER, similar to
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER")
-model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER")
+tokenizer = AutoTokenizer.from_pretrained("LukeGPT88/distilbert-NER")
+model = AutoModelForTokenClassification.from_pretrained("LukeGPT88/distilbert-NER")
 
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 example = "My name is Wolfgang and I live in Berlin"