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community[minor]: Add Ascend NPU optimized Embeddings (#20260)
- **Description:** Add NPU support for embeddings --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
yuncliu
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24
docs/docs/integrations/providers/ascend.mdx
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24
docs/docs/integrations/providers/ascend.mdx
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# Ascend
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>[Ascend](https://https://www.hiascend.com/) is Natural Process Unit provide by Huawei
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This page covers how to use ascend NPU with LangChain.
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### Installation
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Install using torch-npu using:
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```bash
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pip install torch-npu
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```
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Please follow the installation instructions as specified below:
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* Install CANN as shown [here](https://www.hiascend.com/document/detail/zh/canncommercial/700/quickstart/quickstart/quickstart_18_0002.html).
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### Embedding Models
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See a [usage example](/docs/integrations/text_embedding/ascend).
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```python
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from langchain_community.embeddings import AscendEmbeddings
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```
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183
docs/docs/integrations/text_embedding/ascend.ipynb
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docs/docs/integrations/text_embedding/ascend.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a636f6f3-00d7-4248-8c36-3da51190e882",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.04053403 -0.05560051 -0.04385472 ... 0.09371872 0.02846981\n",
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" -0.00576814]\n"
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]
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}
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],
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"source": [
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"from langchain_community.embeddings import AscendEmbeddings\n",
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"\n",
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"model = AscendEmbeddings(\n",
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" model_path=\"/root/.cache/modelscope/hub/yangjhchs/acge_text_embedding\",\n",
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" device_id=0,\n",
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" query_instruction=\"Represend this sentence for searching relevant passages: \",\n",
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")\n",
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"emb = model.embed_query(\"hellow\")\n",
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"print(emb)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8d29ddaa-eef3-4a4e-93d8-0f1c13525fb4",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[-0.00348254 0.03098977 -0.00203087 ... 0.08492374 0.03970494\n",
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" -0.03372753]\n",
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" [-0.02198593 -0.01601127 0.00215684 ... 0.06065163 0.00126425\n",
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" -0.03634358]]\n"
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]
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}
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],
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"source": [
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"doc_embs = model.embed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")\n",
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"print(doc_embs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "797a720d-c478-4254-be2c-975bc4529f57",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<coroutine object Embeddings.aembed_query at 0x7f9fac699cb0>"
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]
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},
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"execution_count": 4,
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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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"model.aembed_query(\"hellow\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "57e62e53-4d2c-4532-9b77-a46bc3da1130",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([-0.04053403, -0.05560051, -0.04385472, ..., 0.09371872,\n",
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" 0.02846981, -0.00576814], dtype=float32)"
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]
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},
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"execution_count": 5,
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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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"await model.aembed_query(\"hellow\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "7e260457-8b50-4ca3-8f76-8a76d8bba8c8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<coroutine object Embeddings.aembed_documents at 0x7fa093ff1a80>"
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]
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},
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"execution_count": 6,
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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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"model.aembed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ce954b94-aaac-4d2c-80be-b2988c16af6d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[-0.00348254, 0.03098977, -0.00203087, ..., 0.08492374,\n",
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" 0.03970494, -0.03372753],\n",
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" [-0.02198593, -0.01601127, 0.00215684, ..., 0.06065163,\n",
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" 0.00126425, -0.03634358]], dtype=float32)"
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]
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},
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"execution_count": 7,
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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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"await model.aembed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7823d69d-de79-4f95-90dd-38f4bdeb9bcc",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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