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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "da5094d4-73fa-4e6c-89a1-0639709d9bc0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sentence: This framework generates embeddings for each input sentence\n",
"Embedding: <class 'numpy.ndarray'> 384\n",
"\n",
"Sentence: Sentences are passed as a list of string.\n",
"Embedding: <class 'numpy.ndarray'> 384\n",
"\n",
"Sentence: The quick brown fox jumps over the lazy dog.\n",
"Embedding: <class 'numpy.ndarray'> 384\n",
"\n"
]
}
],
"source": [
"from sentence_transformers import SentenceTransformer\n",
"model = SentenceTransformer('all-MiniLM-L6-v2')\n",
"\n",
"#Our sentences we like to encode\n",
"sentences = ['This framework generates embeddings for each input sentence',\n",
" 'Sentences are passed as a list of string.',\n",
" 'The quick brown fox jumps over the lazy dog.']\n",
"\n",
"#Sentences are encoded by calling model.encode()\n",
"embeddings = model.encode(sentences)\n",
"\n",
"#Print the embeddings\n",
"for sentence, embedding in zip(sentences, embeddings):\n",
" print(\"Sentence:\", sentence)\n",
" print(\"Embedding:\", type(embedding), embedding.size)\n",
" print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9f519c4d-1a1a-4f74-801d-2bb9e4e14e3a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cosine-Similarity: tensor([[0.6153]])\n"
]
}
],
"source": [
"\n",
"from sentence_transformers import SentenceTransformer, util\n",
"model = SentenceTransformer('all-MiniLM-L6-v2')\n",
"\n",
"#Sentences are encoded by calling model.encode()\n",
"emb1 = model.encode(\"This is a red cat with a hat.\")\n",
"emb2 = model.encode(\"Have you seen my red cat?\")\n",
"\n",
"cos_sim = util.cos_sim(emb1, emb2)\n",
"print(\"Cosine-Similarity:\", cos_sim)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "74e2bf51-6e6d-4d80-8449-6c7d168d561a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top-5 most similar pairs:\n",
"A man is eating food. \t A man is eating a piece of bread. \t 0.7553\n",
"A man is riding a horse. \t A man is riding a white horse on an enclosed ground. \t 0.7369\n",
"A monkey is playing drums. \t Someone in a gorilla costume is playing a set of drums. \t 0.6433\n",
"A woman is playing violin. \t Someone in a gorilla costume is playing a set of drums. \t 0.2564\n",
"A man is eating food. \t A man is riding a horse. \t 0.2474\n"
]
}
],
"source": [
"from sentence_transformers import SentenceTransformer, util\n",
"model = SentenceTransformer('all-MiniLM-L6-v2')\n",
"\n",
"sentences = ['A man is eating food.',\n",
" 'A man is eating a piece of bread.',\n",
" 'The girl is carrying a baby.',\n",
" 'A man is riding a horse.',\n",
" 'A woman is playing violin.',\n",
" 'Two men pushed carts through the woods.',\n",
" 'A man is riding a white horse on an enclosed ground.',\n",
" 'A monkey is playing drums.',\n",
" 'Someone in a gorilla costume is playing a set of drums.'\n",
" ]\n",
"\n",
"#Encode all sentences\n",
"embeddings = model.encode(sentences)\n",
"\n",
"#Compute cosine similarity between all pairs\n",
"cos_sim = util.cos_sim(embeddings, embeddings)\n",
"\n",
"#Add all pairs to a list with their cosine similarity score\n",
"all_sentence_combinations = []\n",
"for i in range(len(cos_sim)-1):\n",
" for j in range(i+1, len(cos_sim)):\n",
" all_sentence_combinations.append([cos_sim[i][j], i, j])\n",
"\n",
"#Sort list by the highest cosine similarity score\n",
"all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)\n",
"\n",
"print(\"Top-5 most similar pairs:\")\n",
"for score, i, j in all_sentence_combinations[0:5]:\n",
" print(\"{} \\t {} \\t {:.4f}\".format(sentences[i], sentences[j], cos_sim[i][j]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1ae46dd-1c19-4385-85b3-ec8f13dc6fe5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ae4f9f4-b9dd-440e-86ec-7ec1ba7166e7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a00a4b61-3e9e-4e92-aa4e-c972b78bfcb8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "07a67248-1f90-4163-98e5-3daf612686d1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 31,
"id": "989ebf4f-1078-4431-b7d7-95d0470b86b0",
"metadata": {},
"outputs": [],
"source": [
"from sentence_transformers import SentenceTransformer\n",
"model = SentenceTransformer('all-distilroberta-v1')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "77ddfd4f-cdf9-4193-a479-d2d2ef86d780",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sentence: This framework generates embeddings for each input sentence\n",
"Embedding: <class 'numpy.ndarray'> 768\n",
"\n",
"Sentence: Sentences are passed as a list of string.\n",
"Embedding: <class 'numpy.ndarray'> 768\n",
"\n",
"Sentence: The quick brown fox jumps over the lazy dog.\n",
"Embedding: <class 'numpy.ndarray'> 768\n",
"\n"
]
}
],
"source": [
"sentences = ['This framework generates embeddings for each input sentence',\n",
" 'Sentences are passed as a list of string.',\n",
" 'The quick brown fox jumps over the lazy dog.']\n",
"\n",
"embeddings = model.encode(sentences)\n",
"\n",
"for sentence, embedding in zip(sentences, embeddings):\n",
" print(\"Sentence:\", sentence)\n",
" print(\"Embedding:\", type(embedding), embedding.size)\n",
" print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff56d32d-9046-41d6-bb92-ac08a176faf2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8de0b905-99ad-4b12-8aa8-76cd2cad8252",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "27a8ed1b-47e0-4de1-b9fb-8e939efff368",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3948830e-5ce7-4d97-9f26-eec9904671e4",
"metadata": {},
"outputs": [],
"source": [
"from sentence_transformers import SentenceTransformer, models\n",
"\n",
"word_embedding_model = models.Transformer('distilroberta-base')\n",
"\n",
"## Step 2: use a pool function over the token embeddings\n",
"pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
"\n",
"## Join steps 1 and 2 using the modules argument\n",
"model = SentenceTransformer(modules=[word_embedding_model, pooling_model])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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