multilingual e5 large instruct embedding model from intfloat
Embedding
34 Pulls Updated 13 days ago
Updated 13 days ago
13 days ago
5f3d8f379743 · 1.1GB
model
archbert
·
parameters559M
·
quantizationF16
1.1GB
license
MIT LICENSE
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
1.0kB
Readme
Usage
After you pull the image, you can simply use the openai compatible embedding endpoint:
curl -vvvv -H 'Content-Type: application/json' "http://localhost:11434/v1/embeddings" -d '{
"input": ["passage: my text 1", "passage: my text 2"],
"model": "jeffh/intfloat-multilingual-e5-large-instruct"
}'
Where model
can also include the tag as necessary (such as jeffh/intfloat-multilingual-e5-large-instruct:f16
)
Model Notes
You should use the structure for queries:
Instruct: {task_description}
Query: {query}
Documents do not need any special structure to be embedded for indexing.