Update README.md

README.md

@@ -2612,7 +2612,7 @@ language:
 
 `nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
 
-
+# Performance Benchmarks
 
 | Name                             | SeqLen | MTEB      | LoCo     | Jina Long Context |  Open Weights | Open Training Code | Open Data   |
 | :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- |
@@ -2624,43 +2624,6 @@ language:
 
 **Exciting Update!**: `nomic-embed-text-v1` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) is aligned to the embedding space of `nomic-embed-text-v1`, meaning any text embedding is multimodal!
 
-## Hosted Inference API
-
-The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
-
-Generating embeddings with the `nomic` Python client is as easy as 
-
-```python
-from nomic import embed
-
-output = embed.text(
-    texts=['Nomic Embedding API', '#keepAIOpen'],
-    model='nomic-embed-text-v1',
-    task_type='search_document'
-)
-
-print(output)
-```
-
-For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
-
-## Data Visualization
-Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
-
-
-[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
-
-## Training Details
-
-We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
-the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
-
-In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
-
-For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
-
-Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
-
 ## Usage
 
 **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. 
@@ -2794,6 +2757,42 @@ const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
 console.log(embeddings);
 ```
 
+## Nomic API
+
+The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
+
+Generating embeddings with the `nomic` Python client is as easy as 
+
+```python
+from nomic import embed
+
+output = embed.text(
+    texts=['Nomic Embedding API', '#keepAIOpen'],
+    model='nomic-embed-text-v1',
+    task_type='search_document'
+)
+
+print(output)
+```
+
+For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
+
+
+## Training
+Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
+
+[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
+
+We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
+the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
+
+In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
+
+For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
+
+Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
+
+
 # Join the Nomic Community
 
 - Nomic: [https://nomic.ai](https://nomic.ai)