diff --git a/README.md b/README.md index 1b8047a..feb9101 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,11 @@ This repo is for code related to the project Document Classification under the W * [`tests`](./tests) Unit tests * ... + + + + + ## Installation 1) Clone this repository: diff --git a/notebooks/CLgeitje.ipynb b/notebooks/CLgeitje.ipynb new file mode 100644 index 0000000..6b88f3f --- /dev/null +++ b/notebooks/CLgeitje.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "!bash /home/azureuser/cloudfiles/code/blobfuse/blobfuse_raadsinformatie.sh" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "\n", + "# Select where to run notebook: \"azure\" or \"local\"\n", + "my_run = \"azure\"\n", + "\n", + "import my_secrets as sc\n", + "import settings as st\n", + "\n", + "if my_run == \"azure\":\n", + " import config_azure as cf\n", + "elif my_run == \"local\":\n", + " import config as cf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load data - just first 2 docs of training set" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelpathidsettexttokenstoken_countclean_tokensclean_tokens_countpdf_pathnum_pagesclean_text
0Motie/home/azureuser/cloudfiles/code/blobfuse/raads...0trainGemeente Amsterdam\\n% Gemeenteraad R\\n% Gemeen...[Gemeente, Amsterdam, %, Gemeenteraad, R, %, G...395[Gemeente, Amsterdam, Gemeenteraad, Gemeentebl...205/home/azureuser/cloudfiles/code/blobfuse/raads...2.0Gemeente Amsterdam Gemeenteraad Gemeenteblad M...
1Motie/home/azureuser/cloudfiles/code/blobfuse/raads...1trainGemeente Amsterdam\\n\\n% Gemeenteraad R\\n\\n% Ge...[Gemeente, Amsterdam, %, Gemeenteraad, R, %, G...390[Gemeente, Amsterdam, Gemeenteraad, Gemeentebl...197/home/azureuser/cloudfiles/code/blobfuse/raads...2.0Gemeente Amsterdam Gemeenteraad Gemeenteblad M...
\n", + "
" + ], + "text/plain": [ + " label path id set \\\n", + "0 Motie /home/azureuser/cloudfiles/code/blobfuse/raads... 0 train \n", + "1 Motie /home/azureuser/cloudfiles/code/blobfuse/raads... 1 train \n", + "\n", + " text \\\n", + "0 Gemeente Amsterdam\\n% Gemeenteraad R\\n% Gemeen... \n", + "1 Gemeente Amsterdam\\n\\n% Gemeenteraad R\\n\\n% Ge... \n", + "\n", + " tokens token_count \\\n", + "0 [Gemeente, Amsterdam, %, Gemeenteraad, R, %, G... 395 \n", + "1 [Gemeente, Amsterdam, %, Gemeenteraad, R, %, G... 390 \n", + "\n", + " clean_tokens clean_tokens_count \\\n", + "0 [Gemeente, Amsterdam, Gemeenteraad, Gemeentebl... 205 \n", + "1 [Gemeente, Amsterdam, Gemeenteraad, Gemeentebl... 197 \n", + "\n", + " pdf_path num_pages \\\n", + "0 /home/azureuser/cloudfiles/code/blobfuse/raads... 2.0 \n", + "1 /home/azureuser/cloudfiles/code/blobfuse/raads... 2.0 \n", + "\n", + " clean_text \n", + "0 Gemeente Amsterdam Gemeenteraad Gemeenteblad M... \n", + "1 Gemeente Amsterdam Gemeenteraad Gemeenteblad M... " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_pickle(f\"{cf.output_path}/txtfiles.pkl\")\n", + "df = df.loc[df['set']=='train'].head(2)\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tryout GEITje\n", + "Load chatbot" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/accelerate/utils/modeling.py:1341: UserWarning: Current model requires 1073750016 bytes of buffer for offloaded layers, which seems does not fit any GPU's remaining memory. If you are experiencing a OOM later, please consider using offload_buffers=True.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "89031b8ea8de40e3adaa0ba64da30641", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mconfig_azure\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcf\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m my_run \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocal\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcf\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'config_azure'" + ] + } + ], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "\n", + "# Select where to run notebook: \"azure\" or \"local\"\n", + "my_run = \"azure\"\n", + "\n", + "# import my_secrets as sc\n", + "import settings as st\n", + "\n", + "if my_run == \"azure\":\n", + " import config_azure as cf\n", + "elif my_run == \"local\":\n", + " import config as cf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tryout GEITje" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load data -- first only select training data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Counter\n\u001b[0;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_pickle\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_path\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/txtfiles.pkl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mloc[df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(Counter(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/pandas/io/pickle.py:185\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;124;03mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m 125\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;124;03m4 4 9\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 184\u001b[0m excs_to_catch \u001b[38;5;241m=\u001b[39m (\u001b[38;5;167;01mAttributeError\u001b[39;00m, \u001b[38;5;167;01mImportError\u001b[39;00m, \u001b[38;5;167;01mModuleNotFoundError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m)\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 188\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m 192\u001b[0m \u001b[38;5;66;03m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;66;03m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 197\u001b[0m \u001b[38;5;66;03m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/pandas/io/common.py:882\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m 874\u001b[0m handle,\n\u001b[1;32m 875\u001b[0m ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 878\u001b[0m newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 879\u001b[0m )\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 883\u001b[0m handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[1;32m 885\u001b[0m \u001b[38;5;66;03m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import pandas as pd\n", + "from collections import Counter\n", + "df = pd.read_pickle(f\"{cf.output_path}/txtfiles.pkl\")\n", + "df = df.loc[df['set']=='train']\n", + "\n", + "print(Counter(df['label']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n" + ] + } + ], + "source": [ + "print(len(Counter(df['label'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-02 12:41:39.227811: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2024-04-02 12:41:39.227841: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9761f40091974abdbca1d16ac8d45118", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors.index.json: 0%| | 0.00/23.9k [00:00 4\u001b[0m chatbot \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconversational\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mRijgersberg/GEITje-7B-chat-v2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/transformers/pipelines/__init__.py:905\u001b[0m, in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m 903\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m framework \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 904\u001b[0m model_classes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[0;32m--> 905\u001b[0m framework, model \u001b[38;5;241m=\u001b[39m \u001b[43minfer_framework_load_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 906\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 907\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_classes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 908\u001b[0m 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hub_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39m_commit_hash\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/transformers/pipelines/base.py:279\u001b[0m, in \u001b[0;36minfer_framework_load_model\u001b[0;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[1;32m 273\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m 274\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel might be a PyTorch model (ending with `.bin`) but PyTorch is not available. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrying to load the model with Tensorflow.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 276\u001b[0m )\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 279\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(model, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 281\u001b[0m model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39meval()\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py:561\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m 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+ "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/transformers/modeling_utils.py:3502\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 3493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_orig \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3494\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_default_dtype(dtype_orig)\n\u001b[1;32m 3495\u001b[0m (\n\u001b[1;32m 3496\u001b[0m model,\n\u001b[1;32m 3497\u001b[0m missing_keys,\n\u001b[1;32m 3498\u001b[0m unexpected_keys,\n\u001b[1;32m 3499\u001b[0m mismatched_keys,\n\u001b[1;32m 3500\u001b[0m offload_index,\n\u001b[1;32m 3501\u001b[0m error_msgs,\n\u001b[0;32m-> 3502\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_pretrained_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3503\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3504\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3505\u001b[0m \u001b[43m \u001b[49m\u001b[43mloaded_state_dict_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# XXX: rename?\u001b[39;49;00m\n\u001b[1;32m 3506\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3507\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3508\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_mismatched_sizes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_mismatched_sizes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3509\u001b[0m \u001b[43m 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\u001b[49m\u001b[43moffload_state_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_state_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3515\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3516\u001b[0m \u001b[43m \u001b[49m\u001b[43mhf_quantizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhf_quantizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3517\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3518\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3520\u001b[0m \u001b[38;5;66;03m# make sure token embedding weights are still tied if needed\u001b[39;00m\n\u001b[1;32m 3521\u001b[0m model\u001b[38;5;241m.\u001b[39mtie_weights()\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/transformers/modeling_utils.py:3926\u001b[0m, in \u001b[0;36mPreTrainedModel._load_pretrained_model\u001b[0;34m(cls, model, state_dict, loaded_keys, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, _fast_init, low_cpu_mem_usage, device_map, offload_folder, offload_state_dict, dtype, hf_quantizer, keep_in_fp32_modules)\u001b[0m\n\u001b[1;32m 3924\u001b[0m hf_quantizer\u001b[38;5;241m.\u001b[39mcreate_quantized_param(model, param, key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m, state_dict)\n\u001b[1;32m 3925\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3926\u001b[0m new_error_msgs, offload_index, state_dict_index \u001b[38;5;241m=\u001b[39m \u001b[43m_load_state_dict_into_meta_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3927\u001b[0m \u001b[43m 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3935\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate_dict_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3936\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate_dict_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3937\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3938\u001b[0m \u001b[43m \u001b[49m\u001b[43mhf_quantizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhf_quantizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3939\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_safetensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_safetensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3940\u001b[0m \u001b[43m 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unexpected_keys)\u001b[0m\n\u001b[1;32m 798\u001b[0m state_dict_index \u001b[38;5;241m=\u001b[39m offload_weight(param, param_name, model, state_dict_folder, state_dict_index)\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m (\n\u001b[1;32m 800\u001b[0m hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 801\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m hf_quantizer\u001b[38;5;241m.\u001b[39mrequires_parameters_quantization)\n\u001b[1;32m 802\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m hf_quantizer\u001b[38;5;241m.\u001b[39mcheck_quantized_param(model, param, param_name, state_dict))\n\u001b[1;32m 803\u001b[0m ):\n\u001b[1;32m 804\u001b[0m \u001b[38;5;66;03m# For backward compatibility with older versions of `accelerate` and for non-quantized params\u001b[39;00m\n\u001b[0;32m--> 805\u001b[0m \u001b[43mset_module_tensor_to_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam_device\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mset_module_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 807\u001b[0m hf_quantizer\u001b[38;5;241m.\u001b[39mcreate_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)\n", + "File \u001b[0;32m/anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages/accelerate/utils/modeling.py:387\u001b[0m, in \u001b[0;36mset_module_tensor_to_device\u001b[0;34m(module, tensor_name, device, value, dtype, fp16_statistics, tied_params_map)\u001b[0m\n\u001b[1;32m 385\u001b[0m module\u001b[38;5;241m.\u001b[39m_parameters[tensor_name] \u001b[38;5;241m=\u001b[39m param_cls(new_value, requires_grad\u001b[38;5;241m=\u001b[39mold_value\u001b[38;5;241m.\u001b[39mrequires_grad)\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m--> 387\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 389\u001b[0m new_value \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(value, device\u001b[38;5;241m=\u001b[39mdevice)\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 224.00 MiB. GPU 0 has a total capacity of 14.58 GiB of which 185.56 MiB is free. Including non-PyTorch memory, this process has 7.31 GiB memory in use. Process 146200 has 7.09 GiB memory in use. Of the allocated memory 7.21 GiB is allocated by PyTorch, and 1.77 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" + ] + } + ], + "source": [ + "# import torch\n", + "from transformers import pipeline, Conversation\n", + "\n", + "chatbot = pipeline(task='conversational', model='Rijgersberg/GEITje-7B-chat-v2',\n", + " device_map='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting accelerate\n", + " Downloading accelerate-0.28.0-py3-none-any.whl.metadata (18 kB)\n", + "Requirement already satisfied: numpy>=1.17 in /anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages (from accelerate) (1.23.5)\n", + "Requirement already satisfied: packaging>=20.0 in /anaconda/envs/ThesisAmsterdamEnvironment19/lib/python3.9/site-packages (from accelerate) 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nvidia-cusolver-cu12, torch\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing-extensions 3.7.4.3\n", + " Uninstalling typing-extensions-3.7.4.3:\n", + " Successfully uninstalled typing-extensions-3.7.4.3\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tensorflow 2.5.0 requires numpy~=1.19.2, but you have numpy 1.26.4 which is incompatible.\n", + "tensorflow 2.5.0 requires typing-extensions~=3.7.4, but you have typing-extensions 4.10.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed mpmath-1.3.0 networkx-3.2.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 sympy-1.12 torch-2.2.2 triton-2.2.0 typing-extensions-4.10.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ThesisAmsterdamEnvironment19", + "language": "python", + "name": "thesisamsterdamenvironment19" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/prompt_template.py b/scripts/prompt_template.py new file mode 100644 index 0000000..5b365ed --- /dev/null +++ b/scripts/prompt_template.py @@ -0,0 +1,14 @@ +""" Includes all the prompt templates """ + +# ------ GEITje ------------ +def simple_prompt(classes, doc): + prompt = f""" + Classificeer het document in één van de categoriën. + + Categoriën: {classes} + + Document: + {doc} + + """ + return prompt \ No newline at end of file