TopicModelling_PDF_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "009effac-9d67-4fe6-b3d0-f5ab7ce7d9b0",
   "metadata": {},
   "source": [
    "Too big would have to separate these somehow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5172a3be-57d1-4deb-88a9-9cbf20a334ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open(\"test.json\", \"r\") as f:\n",
    "    dataset = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c141342-380f-4d70-8af6-dddd743aabd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract abstracts to train on and corresponding titles\n",
    "all_texts= [d[\"text\"] for d in dataset]\n",
    "titles = [d[\"file_path\"] for d in dataset]\n",
    "\n",
    "texts = all_texts[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f25e9d67-4478-427f-9907-ae625402bbe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = str(2)\n",
    "device = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27ee8a13-a1b9-49cd-bb7f-5f713c1b1c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "9f61b35ac3a74165b56c43e8d4643637",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import transformers\n",
    "model_id = \"microsoft/Phi-3.5-mini-instruct\"\n",
    "\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\n",
    "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    trust_remote_code=True,\n",
    "    device_map='auto',\n",
    ")\n",
    "model.eval()\n",
    "generator = transformers.pipeline(\n",
    "    model=model, tokenizer=tokenizer,\n",
    "    task='text-generation',\n",
    "    temperature=0.1,\n",
    "    max_new_tokens=500,\n",
    "    repetition_penalty=1.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcf224da-12c6-4dcf-8a0a-ab3363b9ed57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# System prompt describes information given to all conversations\n",
    "system_prompt = \"\"\"\n",
    "<s>[INST] <<SYS>>\n",
    "You are a helpful, respectful and honest assistant for labeling topics.\n",
    "<</SYS>>\n",
    "\"\"\"\n",
    "\n",
    "# Our main prompt with documents ([DOCUMENTS]) and keywords ([KEYWORDS]) tags\n",
    "main_prompt = \"\"\"\n",
    "[INST]\n",
    "I have a topic that contains the following documents:\n",
    "[DOCUMENTS]\n",
    "\n",
    "The topic is described by the following keywords: '[KEYWORDS]'.\n",
    "\n",
    "Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.\n",
    "[/INST]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a869df30-f8b1-406c-9baf-6b847902afd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example prompt demonstrating the output we are looking for\n",
    "example_prompt = \"\"\"\n",
    "I have a topic that contains the following documents:\n",
    "- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.\n",
    "- Meat, but especially beef, is the word food in terms of emissions.\n",
    "- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.\n",
    "\n",
    "The topic is described by the following keywords: 'meat, beef, eat, eating, emissions, steak, food, health, processed, chicken'.\n",
    "\n",
    "Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.\n",
    "\n",
    "[/INST] Environmental impacts of eating meat\n",
    "\"\"\"\n",
    "\n",
    "prompt = system_prompt + example_prompt + main_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a14efaff-4ef6-422f-84c5-dc743d8055f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0fdc6839e6554808adc90c2c3b848edb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cfs/home/u024236/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "# Pre-calculate embeddings\n",
    "embedding_model = SentenceTransformer(\"BAAI/bge-small-en\")\n",
    "embeddings = embedding_model.encode(texts, show_progress_bar=True)\n",
    "\n",
    "from umap import UMAP\n",
    "from hdbscan import HDBSCAN\n",
    "\n",
    "umap_model = UMAP(n_neighbors=10, n_components=5, min_dist=0.0, metric='cosine', random_state=42)\n",
    "hdbscan_model = HDBSCAN(min_cluster_size=10, metric='euclidean', cluster_selection_method='eom', prediction_data=True)\n",
    "\n",
    "# Pre-reduce embeddings for visualization purposes\n",
    "reduced_embeddings = UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine', random_state=42).fit_transform(embeddings)\n",
    "\n",
    "from bertopic.representation import KeyBERTInspired, MaximalMarginalRelevance, TextGeneration\n",
    "\n",
    "# KeyBERT\n",
    "keybert = KeyBERTInspired()\n",
    "\n",
    "# MMR\n",
    "mmr = MaximalMarginalRelevance(diversity=0.3)\n",
    "\n",
    "# Text generation with Llama 2\n",
    "llama2 = TextGeneration(generator, prompt=prompt)\n",
    "\n",
    "# All representation models\n",
    "representation_model = {\n",
    "    \"KeyBERT\": keybert,\n",
    "    \"Llama2\": llama2,\n",
    "    \"MMR\": mmr,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a490067f-7799-47c6-96f7-bcf18140c452",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-02 16:52:56,616 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm\n",
      "2024-10-02 16:52:57,323 - BERTopic - Dimensionality - Completed ✓\n",
      "2024-10-02 16:52:57,324 - BERTopic - Cluster - Start clustering the reduced embeddings\n",
      "2024-10-02 16:52:57,326 - BERTopic - Cluster - Completed ✓\n",
      "2024-10-02 16:52:57,328 - BERTopic - Representation - Extracting topics from clusters using representation models.\n",
      "  0%|                                                                                          | 0/1 [00:00<?, ?it/s]Token indices sequence length is longer than the specified maximum sequence length for this model (1330708 > 131072). Running this sequence through the model will result in indexing errors\n",
      "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` model input instead.\n",
      "  0%|                                                                                          | 0/1 [00:04<?, ?it/s]\n"
     ]
    },
    {
     "ename": "OutOfMemoryError",
     "evalue": "CUDA out of memory. Tried to allocate 6596.69 GiB. GPU 0 has a total capacity of 47.32 GiB of which 17.29 GiB is free. Including non-PyTorch memory, this process has 29.99 GiB memory in use. Of the allocated memory 29.64 GiB is allocated by PyTorch, and 47.00 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)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 17\u001b[0m\n\u001b[1;32m      3\u001b[0m topic_model \u001b[38;5;241m=\u001b[39m BERTopic(\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m   \u001b[38;5;66;03m# Sub-models\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     13\u001b[0m   verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     14\u001b[0m )\n\u001b[1;32m     16\u001b[0m \u001b[38;5;66;03m# Train model\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m topics, probs \u001b[38;5;241m=\u001b[39m \u001b[43mtopic_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtexts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/bertopic/_bertopic.py:492\u001b[0m, in \u001b[0;36mBERTopic.fit_transform\u001b[0;34m(self, documents, embeddings, images, y)\u001b[0m\n\u001b[1;32m    489\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save_representative_docs(custom_documents)\n\u001b[1;32m    490\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    491\u001b[0m     \u001b[38;5;66;03m# Extract topics by calculating c-TF-IDF\u001b[39;00m\n\u001b[0;32m--> 492\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extract_topics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;66;03m# Reduce topics\u001b[39;00m\n\u001b[1;32m    495\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnr_topics:\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/bertopic/_bertopic.py:3984\u001b[0m, in \u001b[0;36mBERTopic._extract_topics\u001b[0;34m(self, documents, embeddings, mappings, verbose)\u001b[0m\n\u001b[1;32m   3982\u001b[0m documents_per_topic \u001b[38;5;241m=\u001b[39m documents\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTopic\u001b[39m\u001b[38;5;124m\"\u001b[39m], as_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39magg({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDocument\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin})\n\u001b[1;32m   3983\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mc_tf_idf_, words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_c_tf_idf(documents_per_topic)\n\u001b[0;32m-> 3984\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopic_representations_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extract_words_per_topic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3985\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_topic_vectors(documents\u001b[38;5;241m=\u001b[39mdocuments, embeddings\u001b[38;5;241m=\u001b[39membeddings, mappings\u001b[38;5;241m=\u001b[39mmappings)\n\u001b[1;32m   3986\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/bertopic/_bertopic.py:4320\u001b[0m, in \u001b[0;36mBERTopic._extract_words_per_topic\u001b[0;34m(self, words, documents, c_tf_idf, calculate_aspects)\u001b[0m\n\u001b[1;32m   4318\u001b[0m         aspects \u001b[38;5;241m=\u001b[39m tuner\u001b[38;5;241m.\u001b[39mextract_topics(\u001b[38;5;28mself\u001b[39m, documents, c_tf_idf, aspects)\n\u001b[1;32m   4319\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(aspect_model, BaseRepresentation):\n\u001b[0;32m-> 4320\u001b[0m     aspects \u001b[38;5;241m=\u001b[39m \u001b[43maspect_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_topics\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc_tf_idf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maspects\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4321\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   4322\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   4323\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsupported type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(aspect_model)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for representation_model[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(aspect)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   4324\u001b[0m     )\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/bertopic/representation/_textgeneration.py:156\u001b[0m, in \u001b[0;36mTextGeneration.extract_topics\u001b[0;34m(self, topic_model, documents, c_tf_idf, topics)\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprompts_\u001b[38;5;241m.\u001b[39mappend(prompt)\n\u001b[1;32m    155\u001b[0m \u001b[38;5;66;03m# Extract result from generator and use that as label\u001b[39;00m\n\u001b[0;32m--> 156\u001b[0m topic_description \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\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[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    157\u001b[0m topic_description \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    158\u001b[0m     (description[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerated_text\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mreplace(prompt, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m), \u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m description \u001b[38;5;129;01min\u001b[39;00m topic_description\n\u001b[1;32m    159\u001b[0m ]\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(topic_description) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m10\u001b[39m:\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/pipelines/text_generation.py:272\u001b[0m, in \u001b[0;36mTextGenerationPipeline.__call__\u001b[0;34m(self, text_inputs, **kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(chats, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    271\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 272\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtext_inputs\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",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/pipelines/base.py:1268\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\n\u001b[1;32m   1261\u001b[0m         \u001b[38;5;28miter\u001b[39m(\n\u001b[1;32m   1262\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_iterator(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1265\u001b[0m         )\n\u001b[1;32m   1266\u001b[0m     )\n\u001b[1;32m   1267\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1268\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_single\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/pipelines/base.py:1275\u001b[0m, in \u001b[0;36mPipeline.run_single\u001b[0;34m(self, inputs, preprocess_params, forward_params, postprocess_params)\u001b[0m\n\u001b[1;32m   1273\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_single\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs, preprocess_params, forward_params, postprocess_params):\n\u001b[1;32m   1274\u001b[0m     model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreprocess(inputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpreprocess_params)\n\u001b[0;32m-> 1275\u001b[0m     model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\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[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1276\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpostprocess(model_outputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpostprocess_params)\n\u001b[1;32m   1277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m outputs\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/pipelines/base.py:1175\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1173\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1174\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1175\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\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[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1176\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/pipelines/text_generation.py:370\u001b[0m, in \u001b[0;36mTextGenerationPipeline._forward\u001b[0;34m(self, model_inputs, **generate_kwargs)\u001b[0m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeneration_config\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m generate_kwargs:\n\u001b[1;32m    368\u001b[0m     generate_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeneration_config\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgeneration_config\n\u001b[0;32m--> 370\u001b[0m generated_sequence \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\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[43mgenerate_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    371\u001b[0m out_b \u001b[38;5;241m=\u001b[39m generated_sequence\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/generation/utils.py:2048\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   2040\u001b[0m     input_ids, model_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m   2041\u001b[0m         input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   2042\u001b[0m         expand_size\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_return_sequences,\n\u001b[1;32m   2043\u001b[0m         is_encoder_decoder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   2044\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[1;32m   2045\u001b[0m     )\n\u001b[1;32m   2047\u001b[0m     \u001b[38;5;66;03m# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[39;00m\n\u001b[0;32m-> 2048\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2049\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2050\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlogits_processor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_logits_processor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2051\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstopping_criteria\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepared_stopping_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2052\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgeneration_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2053\u001b[0m \u001b[43m        \u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynced_gpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2054\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstreamer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstreamer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2055\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2056\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2058\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m generation_mode \u001b[38;5;129;01min\u001b[39;00m (GenerationMode\u001b[38;5;241m.\u001b[39mBEAM_SAMPLE, GenerationMode\u001b[38;5;241m.\u001b[39mBEAM_SEARCH):\n\u001b[1;32m   2059\u001b[0m     \u001b[38;5;66;03m# 11. prepare beam search scorer\u001b[39;00m\n\u001b[1;32m   2060\u001b[0m     beam_scorer \u001b[38;5;241m=\u001b[39m BeamSearchScorer(\n\u001b[1;32m   2061\u001b[0m         batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m   2062\u001b[0m         num_beams\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mnum_beams,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2067\u001b[0m         max_length\u001b[38;5;241m=\u001b[39mgeneration_config\u001b[38;5;241m.\u001b[39mmax_length,\n\u001b[1;32m   2068\u001b[0m     )\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/generation/utils.py:3008\u001b[0m, in \u001b[0;36mGenerationMixin._sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   3005\u001b[0m model_inputs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_hidden_states\u001b[39m\u001b[38;5;124m\"\u001b[39m: output_hidden_states} \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states \u001b[38;5;28;01melse\u001b[39;00m {})\n\u001b[1;32m   3007\u001b[0m \u001b[38;5;66;03m# forward pass to get next token\u001b[39;00m\n\u001b[0;32m-> 3008\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   3010\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m synced_gpus \u001b[38;5;129;01mand\u001b[39;00m this_peer_finished:\n\u001b[1;32m   3011\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m  \u001b[38;5;66;03m# don't waste resources running the code we don't need\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/microsoft/Phi-3.5-mini-instruct/af0dfb8029e8a74545d0736d30cb6b58d2f0f3f0/modeling_phi3.py:1243\u001b[0m, in \u001b[0;36mPhi3ForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1240\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[1;32m   1242\u001b[0m \u001b[38;5;66;03m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[0;32m-> 1243\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1244\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1245\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1246\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1247\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1248\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1249\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1250\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1251\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1252\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1253\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1255\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1256\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm_head(hidden_states)\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/microsoft/Phi-3.5-mini-instruct/af0dfb8029e8a74545d0736d30cb6b58d2f0f3f0/modeling_phi3.py:1091\u001b[0m, in \u001b[0;36mPhi3Model.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1088\u001b[0m     attention_mask \u001b[38;5;241m=\u001b[39m attention_mask \u001b[38;5;28;01mif\u001b[39;00m (attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01min\u001b[39;00m attention_mask) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1089\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1090\u001b[0m     \u001b[38;5;66;03m# 4d mask is passed through the layers\u001b[39;00m\n\u001b[0;32m-> 1091\u001b[0m     attention_mask \u001b[38;5;241m=\u001b[39m \u001b[43m_prepare_4d_causal_attention_mask\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1092\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1093\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseq_length\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1094\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1095\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_values_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1096\u001b[0m \u001b[43m        \u001b[49m\u001b[43msliding_window\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msliding_window\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1097\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1099\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m inputs_embeds\n\u001b[1;32m   1101\u001b[0m \u001b[38;5;66;03m# decoder layers\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:327\u001b[0m, in \u001b[0;36m_prepare_4d_causal_attention_mask\u001b[0;34m(attention_mask, input_shape, inputs_embeds, past_key_values_length, sliding_window)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;66;03m# 4d mask is passed through the layers\u001b[39;00m\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(attention_mask\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 327\u001b[0m     attention_mask \u001b[38;5;241m=\u001b[39m \u001b[43mattn_mask_converter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_4d\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_shape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey_value_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey_value_length\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(attention_mask\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m4\u001b[39m:\n\u001b[1;32m    331\u001b[0m     expected_shape \u001b[38;5;241m=\u001b[39m (input_shape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m1\u001b[39m, input_shape[\u001b[38;5;241m1\u001b[39m], key_value_length)\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:123\u001b[0m, in \u001b[0;36mAttentionMaskConverter.to_4d\u001b[0;34m(self, attention_mask_2d, query_length, dtype, key_value_length)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    119\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    120\u001b[0m         )\n\u001b[1;32m    122\u001b[0m     past_key_values_length \u001b[38;5;241m=\u001b[39m key_value_length \u001b[38;5;241m-\u001b[39m query_length\n\u001b[0;32m--> 123\u001b[0m     causal_4d_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_causal_mask\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    124\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_shape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask_2d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_values_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[43m        \u001b[49m\u001b[43msliding_window\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msliding_window\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msliding_window \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    131\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSliding window is currently only implemented for causal masking\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Documents/TextSummarizer/summarize_env/lib64/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:158\u001b[0m, in \u001b[0;36mAttentionMaskConverter._make_causal_mask\u001b[0;34m(input_ids_shape, dtype, device, past_key_values_length, sliding_window)\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;124;03mMake causal mask used for bi-directional self-attention.\u001b[39;00m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    157\u001b[0m bsz, tgt_len \u001b[38;5;241m=\u001b[39m input_ids_shape\n\u001b[0;32m--> 158\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfull\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtgt_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtgt_len\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    159\u001b[0m mask_cond \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39marange(mask\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m    160\u001b[0m mask\u001b[38;5;241m.\u001b[39mmasked_fill_(mask_cond \u001b[38;5;241m<\u001b[39m (mask_cond \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mview(mask\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), \u001b[38;5;241m1\u001b[39m), \u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 6596.69 GiB. GPU 0 has a total capacity of 47.32 GiB of which 17.29 GiB is free. Including non-PyTorch memory, this process has 29.99 GiB memory in use. Of the allocated memory 29.64 GiB is allocated by PyTorch, and 47.00 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": [
    "from bertopic import BERTopic\n",
    "\n",
    "topic_model = BERTopic(\n",
    "\n",
    "  # Sub-models\n",
    "  embedding_model=embedding_model,\n",
    "  umap_model=umap_model,\n",
    "  hdbscan_model=hdbscan_model,\n",
    "  representation_model=representation_model,\n",
    "\n",
    "  # Hyperparameters\n",
    "  top_n_words=10,\n",
    "  verbose=True\n",
    ")\n",
    "\n",
    "# Train model\n",
    "topics, probs = topic_model.fit_transform(texts, embeddings)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26512016-0722-4eb3-8d9c-b4c58d0863ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show topics\n",
    "topic_model.get_topic_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72e97b7d-aa8b-4d21-9591-a89aebf4804c",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_model.get_topic(1, full=True)[\"KeyBERT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0b3ea90-ce7e-4765-adb7-bafa64fb6380",
   "metadata": {},
   "outputs": [],
   "source": [
    "llama2_labels = [label[0][0].split(\"\\n\")[0] for label in topic_model.get_topics(full=True)[\"Llama2\"].values()]\n",
    "topic_model.set_topic_labels(llama2_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "905257e0-a109-479b-8d19-f48323a2c077",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_model.visualize_documents(titles, reduced_embeddings=reduced_embeddings, hide_annotations=True, hide_document_hover=False, custom_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bccf30e-3f82-4825-b41b-4d5c2cdaa839",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7cc5848-7cea-4ae4-8a89-1b0015899b13",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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