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{
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"id": "72e26e1c-3fda-4333-9401-4ee15d1bf3b8",
"metadata": {},
"outputs": [],
"source": [
"model_id = \"sentence-transformers/all-MiniLM-L6-v2\"\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "68f1d64b-12b2-4d2a-a06a-6a7d35c3a6f5",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"api_url = f\"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}\"\n",
"headers = {\"Authorization\": f\"Bearer {hf_token}\"}"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "aae00acb-6932-4b46-bc99-a542740c0f33",
"metadata": {},
"outputs": [],
"source": [
"def query(texts):\n",
" response = requests.post(api_url, headers=headers, json={\"inputs\": texts, \"options\":{\"wait_for_model\":True}})\n",
" return response.json()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "3955111a-6785-4375-b6cd-126f5bd9afd4",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import pandas as pd\n",
"\n",
"# Pfad zur CSV-Datei\n",
"file_path = '/Users/I569961/Downloads/imdb_top_1000.csv'\n",
"\n",
"# Einlesen der CSV-Datei\n",
"data = pd.read_csv(file_path)\n",
"\n",
"# Konvertieren des DataFrame in einen String\n",
"data_string = data.to_string()\n",
"\n",
"output = query(texts)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "c5615ec3-2168-420c-9146-77c37b8df8fb",
"metadata": {},
"outputs": [
{
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" <td>0.041565</td>\n",
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" <td>-0.000133</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13 rows × 384 columns</p>\n",
"</div>"
],
"text/plain": [
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"\n",
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"11 0.006544 -0.049057 -0.031649 ... -0.041085 -0.008593 -0.021544 -0.021112 \n",
"12 -0.041175 -0.042124 -0.102631 ... -0.135239 0.013612 -0.049410 -0.006925 \n",
"\n",
" 378 379 380 381 382 383 \n",
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"4 0.008436 0.019169 0.048212 -0.040412 0.083346 0.026855 \n",
"5 0.039657 0.026384 0.045514 0.059089 -0.017509 0.007166 \n",
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"8 -0.030168 0.039412 0.044993 0.000578 -0.025124 0.034191 \n",
"9 -0.030137 0.006779 0.054854 0.029937 0.070214 0.041565 \n",
"10 -0.091150 0.001324 0.053866 -0.083904 0.037684 0.002314 \n",
"11 -0.019502 0.050040 -0.029175 0.005498 0.152892 0.024720 \n",
"12 0.085355 -0.007875 0.030402 -0.029012 0.001888 -0.000133 \n",
"\n",
"[13 rows x 384 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"embeddings = pd.DataFrame(output)\n",
"embeddings"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "9ac9b882-df78-4bda-8ae5-257171d9890c",
"metadata": {},
"outputs": [],
"source": [
"embeddings.to_csv(\"embeddings.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "6d573657-d483-441b-afbb-643f487815c5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 4.9906e-04, 3.8390e-02, -3.8530e-02, -4.8088e-02, 1.2520e-02,\n",
" 9.9632e-03, 3.6153e-03, 6.6720e-02, -5.6117e-02, -2.8780e-02,\n",
" 2.7026e-02, -4.5948e-02, -3.1805e-02, 3.7404e-02, 1.4018e-01,\n",
" 7.4267e-02, 5.3000e-03, -3.6980e-02, 1.1275e-03, -8.6605e-02,\n",
" -6.6038e-02, 4.8514e-02, 6.1135e-03, 5.5289e-03, -4.0952e-02,\n",
" -3.3284e-02, 4.5587e-02, 3.3399e-02, -5.0551e-02, 1.5164e-02,\n",
" 8.0977e-02, 3.3518e-02, -1.7935e-01, 4.1962e-02, -1.6916e-02,\n",
" 1.1325e-02, -4.0028e-02, -4.4969e-02, 8.2986e-02, 4.9503e-02,\n",
" 1.1134e-01, 5.6467e-03, 2.0734e-03, -3.6509e-02, 2.9235e-02,\n",
" 1.1157e-01, -8.2191e-02, -1.8864e-02, 3.1457e-02]])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"\n",
"question = [\"Best action movie?\"]\n",
"output = query(question)\n",
"\n",
"query_embeddings = torch.FloatTensor(output)\n",
"query_embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "beb947b2-5cf2-42f8-bfc0-e2c5dd33ba49",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ad5f317-769d-41b6-8dcb-eaf3983e1f8a",
"metadata": {},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "833f206f-bd61-41ab-a79e-c0fdf52159d1",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|