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fastembed/docs/examples/Supported_Models.ipynb
Andrey Vasnetsov 29cfcda056 add examples with supported typed of models into readme (#271) 
* add examples with supported typed of models into readme

* fix link

* Update README.md

Co-authored-by: George <george.panchuk@qdrant.tech>

* Update README.md

Co-authored-by: George <george.panchuk@qdrant.tech>

* Update README.md

Co-authored-by: George <george.panchuk@qdrant.tech>

---------

Co-authored-by: George <george.panchuk@qdrant.tech>
2024-06-13 10:58:48 +02:00

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{
"cells": [
{
"cell_type": "code",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
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"metadata": {
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"source": [
"import pandas as pd\n",
"\n",
"from fastembed import (\n",
" SparseTextEmbedding,\n",
" TextEmbedding,\n",
" LateInteractionTextEmbedding,\n",
" ImageEmbedding,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Supported Text Embedding Models"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2024-05-31T18:13:25.863008Z",
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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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BAAI/bge-small-en-v1.5</td>\n",
" <td>384</td>\n",
" <td>Fast and Default English model</td>\n",
" <td>0.067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BAAI/bge-small-zh-v1.5</td>\n",
" <td>512</td>\n",
" <td>Fast and recommended Chinese model</td>\n",
" <td>0.090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</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>0.090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>snowflake/snowflake-arctic-embed-xs</td>\n",
" <td>384</td>\n",
" <td>Based on all-MiniLM-L6-v2 model with only 22m ...</td>\n",
" <td>0.090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>jinaai/jina-embeddings-v2-small-en</td>\n",
" <td>512</td>\n",
" <td>English embedding model supporting 8192 sequen...</td>\n",
" <td>0.120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>snowflake/snowflake-arctic-embed-s</td>\n",
" <td>384</td>\n",
" <td>Based on infloat/e5-small-unsupervised, does n...</td>\n",
" <td>0.130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>BAAI/bge-small-en</td>\n",
" <td>384</td>\n",
" <td>Fast English model</td>\n",
" <td>0.130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>nomic-ai/nomic-embed-text-v1.5-Q</td>\n",
" <td>768</td>\n",
" <td>Quantized 8192 context length english model</td>\n",
" <td>0.130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>BAAI/bge-base-en-v1.5</td>\n",
" <td>768</td>\n",
" <td>Base English model, v1.5</td>\n",
" <td>0.210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
" <td>384</td>\n",
" <td>Sentence Transformer model, paraphrase-multili...</td>\n",
" <td>0.220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Qdrant/clip-ViT-B-32-text</td>\n",
" <td>512</td>\n",
" <td>CLIP text encoder</td>\n",
" <td>0.250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>BAAI/bge-base-en</td>\n",
" <td>768</td>\n",
" <td>Base English model</td>\n",
" <td>0.420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>snowflake/snowflake-arctic-embed-m</td>\n",
" <td>768</td>\n",
" <td>Based on intfloat/e5-base-unsupervised model, ...</td>\n",
" <td>0.430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>nomic-ai/nomic-embed-text-v1</td>\n",
" <td>768</td>\n",
" <td>8192 context length english model</td>\n",
" <td>0.520</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 sequen...</td>\n",
" <td>0.520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</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>0.520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>snowflake/snowflake-arctic-embed-m-long</td>\n",
" <td>768</td>\n",
" <td>Based on nomic-ai/nomic-embed-text-v1-unsuperv...</td>\n",
" <td>0.540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>mixedbread-ai/mxbai-embed-large-v1</td>\n",
" <td>1024</td>\n",
" <td>MixedBread Base sentence embedding model, does...</td>\n",
" <td>0.640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
" <td>768</td>\n",
" <td>Sentence-transformers model for tasks like clu...</td>\n",
" <td>1.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>snowflake/snowflake-arctic-embed-l</td>\n",
" <td>1024</td>\n",
" <td>Based on intfloat/e5-large-unsupervised, large...</td>\n",
" <td>1.020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>BAAI/bge-large-en-v1.5</td>\n",
" <td>1024</td>\n",
" <td>Large English model, v1.5</td>\n",
" <td>1.200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>thenlper/gte-large</td>\n",
" <td>1024</td>\n",
" <td>Large general text embeddings model</td>\n",
" <td>1.200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>intfloat/multilingual-e5-large</td>\n",
" <td>1024</td>\n",
" <td>Multilingual model, e5-large. Recommend using ...</td>\n",
" <td>2.240</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model dim \\\n",
"0 BAAI/bge-small-en-v1.5 384 \n",
"1 BAAI/bge-small-zh-v1.5 512 \n",
"2 sentence-transformers/all-MiniLM-L6-v2 384 \n",
"3 snowflake/snowflake-arctic-embed-xs 384 \n",
"4 jinaai/jina-embeddings-v2-small-en 512 \n",
"5 snowflake/snowflake-arctic-embed-s 384 \n",
"6 BAAI/bge-small-en 384 \n",
"7 nomic-ai/nomic-embed-text-v1.5-Q 768 \n",
"8 BAAI/bge-base-en-v1.5 768 \n",
"9 sentence-transformers/paraphrase-multilingual-... 384 \n",
"10 Qdrant/clip-ViT-B-32-text 512 \n",
"11 BAAI/bge-base-en 768 \n",
"12 snowflake/snowflake-arctic-embed-m 768 \n",
"13 nomic-ai/nomic-embed-text-v1 768 \n",
"14 jinaai/jina-embeddings-v2-base-en 768 \n",
"15 nomic-ai/nomic-embed-text-v1.5 768 \n",
"16 snowflake/snowflake-arctic-embed-m-long 768 \n",
"17 mixedbread-ai/mxbai-embed-large-v1 1024 \n",
"18 sentence-transformers/paraphrase-multilingual-... 768 \n",
"19 snowflake/snowflake-arctic-embed-l 1024 \n",
"20 BAAI/bge-large-en-v1.5 1024 \n",
"21 thenlper/gte-large 1024 \n",
"22 intfloat/multilingual-e5-large 1024 \n",
"\n",
" description size_in_GB \n",
"0 Fast and Default English model 0.067 \n",
"1 Fast and recommended Chinese model 0.090 \n",
"2 Sentence Transformer model, MiniLM-L6-v2 0.090 \n",
"3 Based on all-MiniLM-L6-v2 model with only 22m ... 0.090 \n",
"4 English embedding model supporting 8192 sequen... 0.120 \n",
"5 Based on infloat/e5-small-unsupervised, does n... 0.130 \n",
"6 Fast English model 0.130 \n",
"7 Quantized 8192 context length english model 0.130 \n",
"8 Base English model, v1.5 0.210 \n",
"9 Sentence Transformer model, paraphrase-multili... 0.220 \n",
"10 CLIP text encoder 0.250 \n",
"11 Base English model 0.420 \n",
"12 Based on intfloat/e5-base-unsupervised model, ... 0.430 \n",
"13 8192 context length english model 0.520 \n",
"14 English embedding model supporting 8192 sequen... 0.520 \n",
"15 8192 context length english model 0.520 \n",
"16 Based on nomic-ai/nomic-embed-text-v1-unsuperv... 0.540 \n",
"17 MixedBread Base sentence embedding model, does... 0.640 \n",
"18 Sentence-transformers model for tasks like clu... 1.000 \n",
"19 Based on intfloat/e5-large-unsupervised, large... 1.020 \n",
"20 Large English model, v1.5 1.200 \n",
"21 Large general text embeddings model 1.200 \n",
"22 Multilingual model, e5-large. Recommend using ... 2.240 "
]
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"source": [
"supported_models = (\n",
" pd.DataFrame(TextEmbedding.list_supported_models())\n",
" .sort_values(\"size_in_GB\")\n",
" .drop(columns=[\"sources\", \"model_file\", \"additional_files\"])\n",
" .reset_index(drop=True)\n",
")\n",
"supported_models"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Supported Sparse Text Embedding Models"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2024-05-31T18:13:27.124747Z",
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" <td>Qdrant/bm42-all-minilm-l6-v2-attentions</td>\n",
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" <td>0.090</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>prithvida/Splade_PP_en_v1</td>\n",
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" <td>Misspelled version of the model. Retained for ...</td>\n",
" <td>0.532</td>\n",
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" <th>2</th>\n",
" <td>prithivida/Splade_PP_en_v1</td>\n",
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"text/plain": [
" model vocab_size \\\n",
"0 Qdrant/bm42-all-minilm-l6-v2-attentions 30522 \n",
"1 prithvida/Splade_PP_en_v1 30522 \n",
"2 prithivida/Splade_PP_en_v1 30522 \n",
"\n",
" description size_in_GB \n",
"0 Light sparse embedding model, which assigns an... 0.090 \n",
"1 Misspelled version of the model. Retained for ... 0.532 \n",
"2 Independent Implementation of SPLADE++ Model f... 0.532 "
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"source": [
"## Supported Late Interaction Text Embedding Models"
]
},
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"cell_type": "code",
"execution_count": 10,
"metadata": {
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" <th>0</th>\n",
" <td>colbert-ir/colbertv2.0</td>\n",
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"## Supported Image Embedding Models"
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" model dim \\\n",
"0 Qdrant/resnet50-onnx 2048 \n",
"1 Qdrant/clip-ViT-B-32-vision 512 \n",
"\n",
" description size_in_GB \n",
"0 ResNet-50 from `Deep Residual Learning for Ima... 0.10 \n",
"1 CLIP vision encoder based on ViT-B/32 0.34 "
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"metadata": {},
"output_type": "execute_result"
}
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"(\n",
" pd.DataFrame(ImageEmbedding.list_supported_models()).sort_values(\"size_in_GB\")\n",
" .drop(columns=[\"sources\", \"model_file\"])\n",
" .reset_index(drop=True)\n",
")"
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}
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