Total runs:
16.1M
Run Growth:
1.6M
Growth Rate:
9.74%
Sponsored by
PostMcpAi
nomic-ai / nomic-embed-text-v1-unsupervised
Model's Last Updated: August 02 2024
sentence-similarity
Introduction of nomic-embed-text-v1-unsupervised
Model Details of nomic-embed-text-v1-unsupervised
nomic-embed-text-v1-unsupervised: A Reproducible Long Context (8192) Text Embedder
nomic-embed-text-v1-unsupervised
is 8192 context length text encoder. This is a checkpoint after contrastive pretraining from multi-stage contrastive training of the
final model
. If you want to extract embeddings, we suggest using
nomic-embed-text-v1
.
| Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data |
|---|---|---|---|---|---|---|---|
| nomic-embed-text-v1 | 8192 | 62.39 | 85.53 | 54.16 | ✅ | ✅ | ✅ |
| jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ |
| text-embedding-3-small | 8191 | 62.26 | 82.40 | 58.20 | ❌ | ❌ | ❌ |
| text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ |
If you would like to finetune a model on more data, you can use this model as an initialization
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
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
Data Visualization
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
Training Details
We train our embedder using a multi-stage training pipeline. Starting from a long-context BERT model , 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 and corresponding blog post .
Training data to train the models is released in its entirety. For more details, see the
contrastors
repository
Usage
Note
nomic-embed-text
requires prefixes! We support the prefixes
[search_query, search_document, classification, clustering]
.
For retrieval applications, you should prepend
search_document
for all your documents and
search_query
for your queries.
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-unsupervised", trust_remote_code=True)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
Transformers
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True)
model.eval()
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
The model natively supports scaling of the sequence length past 2048 tokens. To do so,
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True)
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True, rotary_scaling_factor=2)
Join the Nomic Community
- Nomic: https://nomic.ai
- Discord: https://discord.gg/myY5YDR8z8
- Twitter: https://twitter.com/nomic_ai
Runs of nomic-ai nomic-embed-text-v1-unsupervised on huggingface.co
611
Total runs
0
24-hour runs
4
3-day runs
-9
7-day runs
-124
30-day runs
More Information About nomic-embed-text-v1-unsupervised huggingface.co Model
Url of nomic-embed-text-v1-unsupervised
nomic-embed-text-v1-unsupervised huggingface.co Url
Provider of nomic-embed-text-v1-unsupervised huggingface.co
Other API from nomic-ai
Updated:April 07 2026
Total runs:
3.5M
Run Growth:
-899.2K
Growth Rate:
-24.81%
Total runs:
2.1M
Run Growth:
949.0K
Growth Rate:
44.91%
Total runs:
409.2K
Run Growth:
154.9K
Growth Rate:
37.85%
Total runs:
212.7K
Run Growth:
-145.0K
Growth Rate:
-69.00%
Total runs:
155.6K
Run Growth:
27.6K
Growth Rate:
18.31%
Total runs:
77.6K
Run Growth:
15.4K
Growth Rate:
19.78%
Total runs:
50.6K
Run Growth:
39.3K
Growth Rate:
79.54%
Total runs:
7.0K
Run Growth:
1.6K
Growth Rate:
22.54%
Total runs:
2.7K
Run Growth:
-181
Growth Rate:
-6.65%
Total runs:
2.7K
Run Growth:
-306
Growth Rate:
-11.33%
Total runs:
2.4K
Run Growth:
1.1K
Growth Rate:
46.71%
Total runs:
2.2K
Run Growth:
737
Growth Rate:
81.89%
Total runs:
1.5K
Run Growth:
686
Growth Rate:
44.43%
Total runs:
1.1K
Run Growth:
-309
Growth Rate:
-28.40%
Total runs:
1.1K
Run Growth:
-381
Growth Rate:
-35.74%
Total runs:
165
Run Growth:
89
Growth Rate:
53.29%
Total runs:
141
Run Growth:
6
Growth Rate:
4.26%
Total runs:
115
Run Growth:
-91
Growth Rate:
-75.83%
Total runs:
50
Run Growth:
7
Growth Rate:
14.00%
Total runs:
50
Run Growth:
7
Growth Rate:
14.00%
Total runs:
48
Run Growth:
4
Growth Rate:
8.33%
Total runs:
48
Run Growth:
4
Growth Rate:
8.33%
Total runs:
47
Run Growth:
5
Growth Rate:
10.64%
Total runs:
47
Run Growth:
3
Growth Rate:
6.38%
Total runs:
47
Run Growth:
6
Growth Rate:
12.77%
Total runs:
47
Run Growth:
4
Growth Rate:
8.70%
Total runs:
47
Run Growth:
6
Growth Rate:
12.77%
Total runs:
47
Run Growth:
4
Growth Rate:
8.51%
Total runs:
46
Run Growth:
4
Growth Rate:
8.70%
Total runs:
46
Run Growth:
3
Growth Rate:
6.52%
Total runs:
46
Run Growth:
2
Growth Rate:
4.35%
Total runs:
46
Run Growth:
4
Growth Rate:
8.70%
Total runs:
46
Run Growth:
5
Growth Rate:
10.87%
Total runs:
46
Run Growth:
5
Growth Rate:
10.87%
Total runs:
45
Run Growth:
1
Growth Rate:
2.22%
Total runs:
45
Run Growth:
0
Growth Rate:
0.00%
Total runs:
45
Run Growth:
1
Growth Rate:
2.27%
Total runs:
44
Run Growth:
2
Growth Rate:
4.55%
Total runs:
44
Run Growth:
3
Growth Rate:
6.82%
Total runs:
44
Run Growth:
0
Growth Rate:
0.00%
Total runs:
43
Run Growth:
0
Growth Rate:
0.00%
Total runs:
42
Run Growth:
-1
Growth Rate:
-2.38%
Total runs:
28
Run Growth:
-71
Growth Rate:
-308.70%
Total runs:
25
Run Growth:
-14
Growth Rate:
-56.00%
Total runs:
24
Run Growth:
6
Growth Rate:
25.00%
Total runs:
23
Run Growth:
4
Growth Rate:
17.39%
Total runs:
22
Run Growth:
4
Growth Rate:
18.18%
Total runs:
17
Run Growth:
-1
Growth Rate:
-5.88%
Total runs:
17
Run Growth:
3
Growth Rate:
17.65%
Total runs:
16
Run Growth:
-1
Growth Rate:
-6.25%
Total runs:
12
Run Growth:
-10
Growth Rate:
-83.33%
Total runs:
11
Run Growth:
-5
Growth Rate:
-45.45%
Total runs:
10
Run Growth:
-2
Growth Rate:
-20.00%
Total runs:
7
Run Growth:
-10
Growth Rate:
-166.67%
Total runs:
0
Run Growth:
-2
Growth Rate:
0.00%