Update README.md

README.md

@@ -387,20 +387,9 @@ model-index:
 
 # bge-large-en-v1.5-sparse
 
-This is the sparsified ONNX variant of the [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference pipeline and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization (INT8) and unstructured pruning (50%). 
+## Usage
 
-Model achieves 98% accuracy recovery on the STSB validation dataset vs. [dense ONNX variant](https://huggingface.co/zeroshot/bge-large-en-v1.5-dense).
-
-Current list of sparse and quantized bge ONNX models:
-
-| Links                                                                                               | Sparsification Method |
-| --------------------------------------------------------------------------------------------------- | ---------------------- |
-| [zeroshot/bge-large-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-large-en-v1.5-sparse)     |    Quantization (INT8) & 50% Pruning                    |
-| [zeroshot/bge-large-en-v1.5-quant](https://huggingface.co/zeroshot/bge-large-en-v1.5-quant)     |   Quantization (INT8)                     |
-| [zeroshot/bge-base-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-base-en-v1.5-sparse)     |   Quantization (INT8) & 50% Pruning                     |
-| [zeroshot/bge-base-en-v1.5-quant](https://huggingface.co/zeroshot/bge-base-en-v1.5-quant)     |     Quantization (INT8)                    |
-| [zeroshot/bge-small-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-small-en-v1.5-sparse) |    Quantization (INT8) & 50% Pruning                    |
-| [zeroshot/bge-small-en-v1.5-quant](https://huggingface.co/zeroshot/bge-small-en-v1.5-quant) |     Quantization (INT8)                    |
+This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization/pruning and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
 
 ```bash
 pip install -U deepsparse-nightly[sentence_transformers]
@@ -408,7 +397,7 @@ pip install -U deepsparse-nightly[sentence_transformers]
 
 ```python
 from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
-model = DeepSparseSentenceTransformer('zeroshot/bge-large-en-v1.5-sparse', export=False)
+model = DeepSparseSentenceTransformer('neuralmagic/bge-large-en-v1.5-sparse', export=False)
 
 # Our sentences we like to encode
 sentences = ['This framework generates embeddings for each input sentence',
@@ -425,9 +414,4 @@ for sentence, embedding in zip(sentences, embeddings):
     print("")
 ```
 
-For further details regarding DeepSparse & Sentence Transformers integration, refer to the [DeepSparse README](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers).
-
-
-For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
-
-![;)](https://media.giphy.com/media/bYg33GbNbNIVzSrr84/giphy-downsized-large.gif)
+For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).