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https://github.com/qdrant/fastembed.git
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55379539ef
* * feat(Supported_Models.ipynb): add support for BAAI/bge-small-zh-v1.5 model * feat(Supported_Models.ipynb): add support for jinaai/jina-embeddings-v2-base-en model * feat(Supported_Models.ip * * chore(docs): update Getting Started.ipynb with progressbar
177 lines
5.9 KiB
Plaintext
177 lines
5.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>model</th>\n",
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" <th>dim</th>\n",
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" <th>description</th>\n",
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" <th>size_in_GB</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>BAAI/bge-small-en</td>\n",
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" <td>384</td>\n",
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" <td>Fast English model</td>\n",
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" <td>0.20</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>BAAI/bge-small-en-v1.5</td>\n",
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" <td>384</td>\n",
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" <td>Fast and Default English model</td>\n",
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" <td>0.13</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>BAAI/bge-small-zh-v1.5</td>\n",
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" <td>512</td>\n",
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" <td>Fast and recommended Chinese model</td>\n",
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" <td>0.10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>BAAI/bge-base-en</td>\n",
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" <td>768</td>\n",
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" <td>Base English model</td>\n",
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" <td>0.50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>BAAI/bge-base-en-v1.5</td>\n",
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" <td>768</td>\n",
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" <td>Base English model, v1.5</td>\n",
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" <td>0.44</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
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" <td>384</td>\n",
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" <td>Sentence Transformer model, MiniLM-L6-v2</td>\n",
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" <td>0.09</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>intfloat/multilingual-e5-large</td>\n",
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" <td>1024</td>\n",
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" <td>Multilingual model, e5-large. Recommend using this model for non-English languages</td>\n",
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" <td>2.24</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>jinaai/jina-embeddings-v2-base-en</td>\n",
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" <td>768</td>\n",
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" <td>English embedding model supporting 8192 sequence length</td>\n",
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" <td>0.55</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>jinaai/jina-embeddings-v2-small-en</td>\n",
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" <td>512</td>\n",
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" <td>English embedding model supporting 8192 sequence length</td>\n",
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" <td>0.13</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" model dim \\\n",
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"0 BAAI/bge-small-en 384 \n",
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"1 BAAI/bge-small-en-v1.5 384 \n",
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"2 BAAI/bge-small-zh-v1.5 512 \n",
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"3 BAAI/bge-base-en 768 \n",
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"4 BAAI/bge-base-en-v1.5 768 \n",
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"5 sentence-transformers/all-MiniLM-L6-v2 384 \n",
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"6 intfloat/multilingual-e5-large 1024 \n",
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"7 jinaai/jina-embeddings-v2-base-en 768 \n",
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"8 jinaai/jina-embeddings-v2-small-en 512 \n",
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"\n",
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" description \\\n",
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"0 Fast English model \n",
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"1 Fast and Default English model \n",
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"2 Fast and recommended Chinese model \n",
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"3 Base English model \n",
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"4 Base English model, v1.5 \n",
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"5 Sentence Transformer model, MiniLM-L6-v2 \n",
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"6 Multilingual model, e5-large. Recommend using this model for non-English languages \n",
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"7 English embedding model supporting 8192 sequence length \n",
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"8 English embedding model supporting 8192 sequence length \n",
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"\n",
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" size_in_GB \n",
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"0 0.20 \n",
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"1 0.13 \n",
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"2 0.10 \n",
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"3 0.50 \n",
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"4 0.44 \n",
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"5 0.09 \n",
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"6 2.24 \n",
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"7 0.55 \n",
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"8 0.13 "
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"from fastembed.embedding import Embedding\n",
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"import pandas as pd\n",
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"pd.set_option('display.max_colwidth', None)\n",
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"pd.DataFrame(Embedding.list_supported_models())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "fst",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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