Rebrand to harpertokenNER and clean up README

README.md

@@ -13,7 +13,7 @@ pipeline_tag: token-classification
 library_name: transformers
 base_model: bert-base-uncased
 model-index:
-  - name: token-classification-ai-fine-tune
+  - name: harpertokenNER
     results:
       - task:
           type: token-classification
@@ -29,15 +29,13 @@ widget:
   - text: "Apple is buying a U.K. startup for $1 billion"
 ---
 
-# token-classification-ai-fine-tune
-
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](https://huggingface.co/bniladridas/token-classification-ai-fine-tune)
+# harpertokenNER
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [CoNLL-2003](https://huggingface.co/datasets/conll2003) dataset. It achieves a validation loss of **0.0474** on the evaluation set.
 
 ## Model Description
 
-This is a token classification model fine-tuned for **Named Entity Recognition (NER)**, built on the `bert-base-uncased` architecture. It’s crafted to identify entities (like people, organizations, and locations) in text, optimized here for CPU accessibility. Uploaded by [bniladridas](https://huggingface.co/bniladridas), it delivers strong NER performance on the CoNLL-2003 benchmark. For a GPU-accelerated version with CUDA support, see the [GitHub repository](https://github.com/bniladridas/token-classification-ai-fine-tune).
+This is a token classification model fine-tuned for **Named Entity Recognition (NER)** on the CoNLL-2003 dataset, built on the `bert-base-uncased` architecture. It identifies entities like people, organizations, and locations in text. Optimized for CPU use. Uploaded by [harpertoken](https://huggingface.co/harpertoken).
 
 ## Intended Uses & Limitations
 
@@ -84,17 +82,4 @@ The following hyperparameters were used during training:
 - **Datasets**: 1.18.3
 - **Tokenizers**: 0.13.3
 
-### Additional Notes
-This version is optimized for CPU use with these intentional adjustments:
-1. **Full-precision training**: Swapped out fp16 for broader compatibility
-2. **Streamlined batch sizes**: Set to 8 for efficient CPU processing
-3. **Simplified workflow**: Skipped gradient accumulation for smoother CPU runs
-4. **Full feature set**: Retained all monitoring (e.g., TensorBoard) and saving capabilities
-
-For the GPU version with CUDA, mixed precision, and gradient accumulation, check out the [GitHub repository](https://github.com/bniladridas/token-classification-ai-fine-tune). To clone it, run:
-
-```bash
-git clone https://github.com/bniladridas/token-classification-ai-fine-tune.git
-```
 
-This model was pushed to the Hugging Face Hub for easy CPU-based deployment.