Update README.md

README.md

@@ -6,9 +6,9 @@ tags:
 - sentence-similarity
 - mteb
 - arctic
-- arctic-embed
+- snowflake-arctic-embed
 model-index:
-- name: snowflake-arctic-embed-xs
+- name: snowflake-snowflake-arctic-embed-xs
   results:
   - task:
       type: Classification
@@ -2822,16 +2822,16 @@ model-index:
 ## News
 
 
-04/16/2024: Release the ** Arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).
+04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/snowflake-arctic-embed).
 
 
 ## Models
 
 
-Arctic-Embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
+snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
 
 
-The `arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
+The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
 
 
 The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report will be available shortly.
@@ -2839,26 +2839,26 @@ The models are trained by leveraging existing open-source text representation mo
 
 | Name                                                                    | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
 | ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |
-| [arctic-embed-xs](https://huggingface.co/Snowflake/arctic-embed-xs/)     | 50.15                            | 22                    | 384                 |
-| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-s/)      | 51.98                            | 33                    | 384                 |
-| [arctic-embed-m](https://huggingface.co/Snowflake/arctic-embed-m/)      | 54.90                            | 110                   | 768                 |
-| [arctic-embed-m-long](https://huggingface.co/Snowflake/arctic-embed-m-long/) | 54.83                            | 137                   | 768                 |
-| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-l/)      | 55.98                            | 335                   | 1024                |
+| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/)     | 50.15                            | 22                    | 384                 |
+| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/)      | 51.98                            | 33                    | 384                 |
+| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)      | 54.90                            | 110                   | 768                 |
+| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83                            | 137                   | 768                 |
+| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)      | 55.98                            | 335                   | 1024                |
 
 
-Aside from being great open-source models, the largest model, [arctic-embed-l](https://huggingface.co/Snowflake/arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
+Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
 
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
 | ------------------------------------------------------------------ | -------------------------------- |
-| [arctic-embed-l](https://huggingface.co/Snowflake/arctic-embed-l/) | 55.98                            |
+| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98                            |
 | Google-gecko-text-embedding                                        | 55.7                             |
 | text-embedding-3-large                                             | 55.44                            |
 | Cohere-embed-english-v3.0                                          | 55.00                            |
 | bge-large-en-v1.5                                                  | 54.29                            |
 
 
-### [Arctic-embed-xs](https://huggingface.co/Snowflake/arctic-embed-xs)
+### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
 
 
 This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
@@ -2866,14 +2866,14 @@ This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://h
 
 | Model Name                                                          | MTEB Retrieval Score (NDCG @ 10) |
 | ------------------------------------------------------------------- | -------------------------------- |
-| [arctic-embed-xs](https://huggingface.co/Snowflake/arctic-embed-xs/) | 50.15                            |
+| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15                            |
 | GIST-all-MiniLM-L6-v2                                               | 45.12                            |
 | gte-tiny                                                            | 44.92                            |
 | all-MiniLM-L6-v2                                                    | 41.95                            |
 | bge-micro-v2                                                        | 42.56                            |
 
 
-### [Arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-s)
+### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)
 
 
 Based on the [infloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
@@ -2881,14 +2881,14 @@ Based on the [infloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
 | ------------------------------------------------------------------ | -------------------------------- |
-| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-s/) | 51.98                            |
+| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98                            |
 | bge-small-en-v1.5                                                  | 51.68                            |
 | Cohere-embed-english-light-v3.0                                    | 51.34                            |
 | text-embedding-3-small                                             | 51.08                            |
 | e5-small-v2                                                        | 49.04                            |
 
 
-### [Arctic-embed-m](https://huggingface.co/Snowflake/arctic-embed-m/)
+### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)
 
 
 Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
@@ -2896,13 +2896,13 @@ Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
 | ------------------------------------------------------------------ | -------------------------------- |
-| [arctic-embed-m](https://huggingface.co/Snowflake/arctic-embed-m/) | 54.90                            |
+| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90                            |
 | bge-base-en-v1.5                                                   | 53.25                            |
 | nomic-embed-text-v1.5                                              | 53.25                            |
 | GIST-Embedding-v0                                                  | 52.31                            |
 | gte-base                                                           | 52.31                            |
 
-### [arctic-embed-m-long](https://huggingface.co/Snowflake/arctic-embed-m-long/)
+### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)
 
 
 Based on the [nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
@@ -2910,14 +2910,14 @@ Based on the [nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
 | ------------------------------------------------------------------ | -------------------------------- |
-| [arctic-embed-m-long](https://huggingface.co/Snowflake/arctic-embed-m-long/) | 54.83                            |
+| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83                            |
 | nomic-embed-text-v1.5                                              | 53.01                            |
 | nomic-embed-text-v1                                                | 52.81                            |
 
 
 
 
-### [arctic-embed-l](https://huggingface.co/Snowflake/arctic-embed-l/)
+### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)
 
 
 Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this small model does not sacrifice retrieval accuracy for its small size.
@@ -2925,7 +2925,7 @@ Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
 | ------------------------------------------------------------------ | -------------------------------- |
-| [arctic-embed-l](https://huggingface.co/Snowflake/arctic-embed-l/) | 55.98                            |
+| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98                            |
 | UAE-Large-V1                                                       | 54.66                            |
 | bge-large-en-v1.5                                                  | 54.29                            |
 | mxbai-embed-large-v1                                               | 54.39                            |
@@ -2938,7 +2938,7 @@ Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5
 ### Using Huggingface transformers
 
 
-You can use the transformers package to use an arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
+You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
 
 
 
@@ -2946,8 +2946,8 @@ You can use the transformers package to use an arctic-embed model, as shown belo
 import torch
 from transformers import AutoModel, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained('Snowflake/arctic-embed-xs')
-model = AutoModel.from_pretrained('Snowflake/arctic-embed-xs', add_pooling_layer=False)
+tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-xs')
+model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-xs', add_pooling_layer=False)
 model.eval()
 
 query_prefix = 'Represent this sentence for searching relevant passages: '