Update README.md

README.md

@@ -19,7 +19,7 @@ library_name: transformers
 
 ### **Description**
 
-The Llama 3.2 NeMo Retriever Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for **multilingual, cross-lingual** text question-answering retrieval, with support for **long documents (up to 8192 tokens)**.  This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.
+The Llama Nemotron Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for **multilingual, cross-lingual** text question-answering retrieval, with support for **long documents (up to 8192 tokens)**.  This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.
 
 
 This model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding model (dense) or lexical search (sparse) index to return relevant text passages given the input. A reranking model can be used to rerank the potential candidate into a final order. The reranking model has the question-passage pairs as an input and therefore, can process cross attention between the words. It’s not feasible to apply a Ranking model on all documents in the knowledge base, therefore, ranking models are often deployed in combination with embedding models.
@@ -28,7 +28,7 @@ This model is a component in a text retrieval system to improve the overall accu
 This model is ready for commercial use.
 
 
-The Llama 3.2 NeMo Retriever Reranking 1B model is a part of the NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for their domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.
+The Llama Nemotron Reranking 1B model is a part of the NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for their domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.
 
 We are excited to announce the open sourcing of this commercial embedding model. For users interested in deploying this model in production environments, it is also available via the model API in NVIDIA Inference Microservices (NIM) at [llama-nemotron-rerank-1b-v2](https://build.nvidia.com/nvidia/llama-3_2-nv-rerankqa-1b-v2).
 
@@ -38,14 +38,14 @@ Use of this model is governed by the [NVIDIA Open Model License Agreement](https
 
 ### **Intended use**
 
-The Llama 3.2 NeMo Retriever Reranking 1B model is most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question.
+The Llama Nemotron Reranking 1B model is most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question.
 
 ### **Model Architecture**
 
 **Architecture Type:** Transformer <br>
 **Network Architecture:** Fine-tuned ranker model from the `meta-llama/Llama-3.2-1B` model.
 
-The Llama 3.2 NeMo Retriever Reranking 1B model is a transformer cross-encoder fine-tuned with contrastive learning. We employ bi-directional attention when fine-tuning for higher accuracy. The last embedding output by the decoder model is used with a mean pooling strategy, and a binary classification head is fine-tuned for the ranking task.
+The Llama Nemotron Reranking 1B model is a transformer cross-encoder fine-tuned with contrastive learning. We employ bi-directional attention when fine-tuning for higher accuracy. The last embedding output by the decoder model is used with a mean pooling strategy, and a binary classification head is fine-tuned for the ranking task.
 
 Ranking models for text ranking are typically trained as a cross-encoder for sentence classification. This involves predicting the relevancy of a sentence pair (for example, question and chunked passages). The CrossEntropy loss is used to maximize the likelihood of passages containing information to answer the question and minimize the likelihood for (negative) passages that do not contain information to answer the question.
 
@@ -161,7 +161,7 @@ for i, (pair, score) in enumerate(zip(pairs, scores)):
 
 ### **Software Integration**
 
-**Runtime:** Llama 3.2 NeMo Retriever Reranking 1B NIM <br>
+**Runtime:** Llama Nemotron Reranking 1B NIM <br>
 **Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace <br>
 **Supported Operating System(s):** Linux