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fastembed/docs/examples/Supported_Models.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>model</th>\n",
" <th>dim</th>\n",
" <th>description</th>\n",
" <th>size_in_GB</th>\n",
" <th>sources</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BAAI/bge-base-en</td>\n",
" <td>768</td>\n",
" <td>Base English model</td>\n",
" <td>0.50</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BAAI/bge-base-en-v1.5</td>\n",
" <td>768</td>\n",
" <td>Base English model, v1.5</td>\n",
" <td>0.44</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz', 'hf': 'qdrant/bge-base-en-v1.5-onnx-q'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BAAI/bge-large-en-v1.5-quantized</td>\n",
" <td>1024</td>\n",
" <td>Large English model, v1.5</td>\n",
" <td>1.34</td>\n",
" <td>{'hf': 'qdrant/bge-large-en-v1.5-onnx-q'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BAAI/bge-large-en-v1.5</td>\n",
" <td>1024</td>\n",
" <td>Large English model, v1.5</td>\n",
" <td>1.34</td>\n",
" <td>{'hf': 'qdrant/bge-large-en-v1.5-onnx'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BAAI/bge-small-en</td>\n",
" <td>384</td>\n",
" <td>Fast English model</td>\n",
" <td>0.20</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>BAAI/bge-small-en-v1.5</td>\n",
" <td>384</td>\n",
" <td>Fast and Default English model</td>\n",
" <td>0.13</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz', 'hf': 'qdrant/bge-small-en-v1.5-onnx-q'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>BAAI/bge-small-zh-v1.5</td>\n",
" <td>512</td>\n",
" <td>Fast and recommended Chinese model</td>\n",
" <td>0.10</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
" <td>384</td>\n",
" <td>Sentence Transformer model, MiniLM-L6-v2</td>\n",
" <td>0.09</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz', 'hf': 'qdrant/all-MiniLM-L6-v2-onnx'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>nomic-ai/nomic-embed-text-v1</td>\n",
" <td>768</td>\n",
" <td>8192 context length english model</td>\n",
" <td>0.54</td>\n",
" <td>{'hf': 'nomic-ai/nomic-embed-text-v1'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>nomic-ai/nomic-embed-text-v1.5</td>\n",
" <td>768</td>\n",
" <td>8192 context length english model</td>\n",
" <td>0.54</td>\n",
" <td>{'hf': 'nomic-ai/nomic-embed-text-v1.5'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>thenlper/gte-large</td>\n",
" <td>1024</td>\n",
" <td>Large general text embeddings model</td>\n",
" <td>1.34</td>\n",
" <td>{'hf': 'qdrant/gte-large-onnx'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>intfloat/multilingual-e5-large</td>\n",
" <td>1024</td>\n",
" <td>Multilingual model, e5-large. Recommend using this model for non-English languages</td>\n",
" <td>2.24</td>\n",
" <td>{'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz', 'hf': 'qdrant/multilingual-e5-large-onnx'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>sentence-transformers/paraphrase-multilingual-mpnet-base-v2</td>\n",
" <td>768</td>\n",
" <td>Sentence-transformers model for tasks like clustering or semantic search</td>\n",
" <td>1.11</td>\n",
" <td>{'hf': 'xenova/paraphrase-multilingual-mpnet-base-v2'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2</td>\n",
" <td>384</td>\n",
" <td>Sentence Transformer model, paraphrase-multilingual-MiniLM-L12-v2</td>\n",
" <td>0.46</td>\n",
" <td>{'hf': 'qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>jinaai/jina-embeddings-v2-base-en</td>\n",
" <td>768</td>\n",
" <td>English embedding model supporting 8192 sequence length</td>\n",
" <td>0.55</td>\n",
" <td>{'hf': 'xenova/jina-embeddings-v2-base-en'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>jinaai/jina-embeddings-v2-small-en</td>\n",
" <td>512</td>\n",
" <td>English embedding model supporting 8192 sequence length</td>\n",
" <td>0.13</td>\n",
" <td>{'hf': 'xenova/jina-embeddings-v2-small-en'}</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model dim \\\n",
"0 BAAI/bge-base-en 768 \n",
"1 BAAI/bge-base-en-v1.5 768 \n",
"2 BAAI/bge-large-en-v1.5-quantized 1024 \n",
"3 BAAI/bge-large-en-v1.5 1024 \n",
"4 BAAI/bge-small-en 384 \n",
"5 BAAI/bge-small-en-v1.5 384 \n",
"6 BAAI/bge-small-zh-v1.5 512 \n",
"7 sentence-transformers/all-MiniLM-L6-v2 384 \n",
"8 nomic-ai/nomic-embed-text-v1 768 \n",
"9 nomic-ai/nomic-embed-text-v1.5 768 \n",
"10 thenlper/gte-large 1024 \n",
"11 intfloat/multilingual-e5-large 1024 \n",
"12 sentence-transformers/paraphrase-multilingual-mpnet-base-v2 768 \n",
"13 sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 384 \n",
"14 jinaai/jina-embeddings-v2-base-en 768 \n",
"15 jinaai/jina-embeddings-v2-small-en 512 \n",
"\n",
" description \\\n",
"0 Base English model \n",
"1 Base English model, v1.5 \n",
"2 Large English model, v1.5 \n",
"3 Large English model, v1.5 \n",
"4 Fast English model \n",
"5 Fast and Default English model \n",
"6 Fast and recommended Chinese model \n",
"7 Sentence Transformer model, MiniLM-L6-v2 \n",
"8 8192 context length english model \n",
"9 8192 context length english model \n",
"10 Large general text embeddings model \n",
"11 Multilingual model, e5-large. Recommend using this model for non-English languages \n",
"12 Sentence-transformers model for tasks like clustering or semantic search \n",
"13 Sentence Transformer model, paraphrase-multilingual-MiniLM-L12-v2 \n",
"14 English embedding model supporting 8192 sequence length \n",
"15 English embedding model supporting 8192 sequence length \n",
"\n",
" size_in_GB \\\n",
"0 0.50 \n",
"1 0.44 \n",
"2 1.34 \n",
"3 1.34 \n",
"4 0.20 \n",
"5 0.13 \n",
"6 0.10 \n",
"7 0.09 \n",
"8 0.54 \n",
"9 0.54 \n",
"10 1.34 \n",
"11 2.24 \n",
"12 1.11 \n",
"13 0.46 \n",
"14 0.55 \n",
"15 0.13 \n",
"\n",
" sources \n",
"0 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz'} \n",
"1 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz', 'hf': 'qdrant/bge-base-en-v1.5-onnx-q'} \n",
"2 {'hf': 'qdrant/bge-large-en-v1.5-onnx-q'} \n",
"3 {'hf': 'qdrant/bge-large-en-v1.5-onnx'} \n",
"4 {'url': 'https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz'} \n",
"5 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-en-v1.5.tar.gz', 'hf': 'qdrant/bge-small-en-v1.5-onnx-q'} \n",
"6 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz'} \n",
"7 {'url': 'https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz', 'hf': 'qdrant/all-MiniLM-L6-v2-onnx'} \n",
"8 {'hf': 'nomic-ai/nomic-embed-text-v1'} \n",
"9 {'hf': 'nomic-ai/nomic-embed-text-v1.5'} \n",
"10 {'hf': 'qdrant/gte-large-onnx'} \n",
"11 {'url': 'https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz', 'hf': 'qdrant/multilingual-e5-large-onnx'} \n",
"12 {'hf': 'xenova/paraphrase-multilingual-mpnet-base-v2'} \n",
"13 {'hf': 'qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q'} \n",
"14 {'hf': 'xenova/jina-embeddings-v2-base-en'} \n",
"15 {'hf': 'xenova/jina-embeddings-v2-small-en'} "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fastembed import TextEmbedding\n",
"import pandas as pd\n",
"\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"pd.DataFrame(TextEmbedding.list_supported_models())"
]
}
],
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