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" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", - " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", - " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", - " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", - "\n", - " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", - "Token: Traceback (most recent call last):\n", - " File \"/usr/local/bin/huggingface-cli\", line 8, in \n", - " sys.exit(main())\n", - " File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/commands/huggingface_cli.py\", line 49, in main\n", - " File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/commands/user.py\", line 98, in run\n", - " login(token=self.args.token, add_to_git_credential=self.args.add_to_git_credential)\n", - " File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/_login.py\", line 112, in login\n", - " interpreter_login(new_session=new_session, write_permission=write_permission)\n", - " File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/_login.py\", line 167, in interpreter_login\n", - " token = getpass(\"Token: \")\n", - " File \"/usr/lib/python3.10/getpass.py\", line 77, in unix_getpass\n", - " passwd = _raw_input(prompt, stream, input=input)\n", - " File \"/usr/lib/python3.10/getpass.py\", line 146, in _raw_input\n", - " line = input.readline()\n", - "KeyboardInterrupt\n", - "^C\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# !pip install tensorflow[and-cuda]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OzNPKFNfG7I1", - "outputId": "e2225ff0-0a2f-4858-bbdd-c28bf5ce8d90" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m417.9/417.9 MB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting nvidia-cufft-cu11==10.9.0.58 (from tensorflow[and-cuda])\n", - " Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.4/168.4 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting nvidia-cudnn-cu11==8.7.0.84 (from tensorflow[and-cuda])\n", - " Downloading nvidia_cudnn_cu11-8.7.0.84-py3-none-manylinux1_x86_64.whl (728.5 MB)\n", - "\u001b[2K \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/728.5 MB\u001b[0m \u001b[31m92.2 MB/s\u001b[0m eta \u001b[36m0:00:07\u001b[0m\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", - "\u001b[2K \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.7/728.5 MB\u001b[0m \u001b[31m90.9 MB/s\u001b[0m eta \u001b[36m0:00:07\u001b[0m\n", - "\u001b[?25h" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install -q accelerate peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7" - ], - "metadata": { - "id": "LQVhFU1sgoiQ" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "import torch\n", - "from datasets import load_dataset\n", - "from transformers import (\n", - " AutoModelForCausalLM,\n", - " AutoTokenizer,\n", - " BitsAndBytesConfig,\n", - " HfArgumentParser,\n", - " TrainingArguments,\n", - " pipeline,\n", - " logging,\n", - ")\n", - "from peft import LoraConfig, PeftModel\n", - "\n", - "\n", - "device = 'cuda' if torch.cuda.is_available() else \"cpu\"\n", - "model = \"deepapaikar/Llama_SCplusQA_10epochs\"\n", - "tokenizer = AutoTokenizer.from_pretrained(model)\n", - "from transformers import AutoModelForCausalLM\n", - "model = AutoModelForCausalLM.from_pretrained(model, load_in_4bit=True)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 104, - "referenced_widgets": [ - "d5fe2a9ae70b4f6db5d41315ec7f5a26", - "a4c0d4522252463ca786f7a206222659", - "965a955b3d9d4f51994144ec9f68a7c4", - 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questionanswer
0Which Yeshiva University school is Dr. Assa Co...Dr. Assa Cohen is joining the Sy Syms School o...
1What is Dr. Assa Cohen's primary research focus?Dr. Assa Cohen's research primarily focuses on...
2Following his entry into Goldstein Golub Kessl...Steve Mayer became the managing partner of the...
3What educational background does Dr. Assa Cohe...Dr. Assa Cohen has a Ph.D. in economics, and h...
4In Dr. Patryk Perkowski's research at the Sy S...Dr. Patryk Perkowski's research primarily focu...
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If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n", - "pip install xformers.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[INST] Where is Yeshiva University? [/INST] New York City Yeshiva University is a top 100 university in the United States. It is located in New York and known for its business and technology programs. It is also known for its central campus, which is in New York City. The university is divided into five schools: Sackstein Center for Dispute Resolution, Stern College for Women, Sy Syms School of Business, Undergraduate Studies, and Wurzweiler School of Social Work. Yeshiva University is a top 100 university in the United States, known for its business and technology programs, located in New York City, and divided into five schools. The five schools include Belfer Center for Policy Studies, Cardozo School of Law, The Ferkauf Graduate School of Psychology, The George Washington University, and Yes\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Assuming your DataFrame is named df\n", - "df['predicted'] = df.apply(lambda row: pipeline(\n", - " task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)(\n", - " f\"[INST] {row['question']} [/INST]\"\n", - ")[0]['generated_text'], axis=1)\n", - "\n", - "# Save the DataFrame to a new CSV file with the 'predicted' column\n", - "df.to_csv('/content/drive/MyDrive/Fall 2023/Model building/Llama/SC+QA Training/Dataset/Test Dataset/output_file.csv', index=False)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 209, - "referenced_widgets": [ - "fd8abef2fc1f4ccbae9b0a71f7dea319", - "8b72d125839641b19759e591bcbdbe5b", - "193dbb2023df43b39d3647349a4a68a1", - "0bae64f6a1d34759a2fe791787e0d56a", - "57a95d2374ae491881b369e26067a663", - "46153b17c1ee4790b3ffa614b8e88072", - "5f734599b8ff4411b3b3cdad2af370eb", - "2c030ccbdd6f4e29b434275d42ee08f8", - "47dd07a964b5431eb914037fa1cb2fbe", - "d5c2cd57309449afb1dfb74f062bd587", - "3deba726004d4b54ba42ecb1b020f3e6", - "4f8d11ee7a834071b0fa4b169aa9d6a5", - "d85891327f4d435f985b8dba42eb631e", - "8360ea973da449dab4c95d71161b5cc8", - "71ac540fd22a4dc1ad2e6a6160bf0de1", - "f3b86733c492443f8e7980edee92a5d4", - "c98519f21d6d4d81af7267213b4f30c7", - "05e778d8671c4379b1145c961215de51", - "6b421322a5b941c0beed5eb7ea6b03df", - "b1537f6bca694d05a3f13e7fb43a4ba0", - "94a6754f78924d35bf9c1b15669107ca", - "e76ae95b037e4a3e85e749efa8b11dcd", - "c12602b102a748619f7d241d10c6bc40", - "564252ee2a384989b65d8317ec3f8bcc", - "39617e9887384f09ae590ed99e0f2daf", - "c3f7f71d97044d2386dbefcf7c376f15", - "82e6ebb638964ddb9e50397a004c82f1", - "e249f0d5f77f49d2aca4afd181aa4dbd", - "bf6c69a196024200beca6d97ebfc77df", - "f572a5218e97493cb20267555d89a3a7", - "f521177077414c23bda9a4d5574fc54f", - "ae9f5251b57b4598988b106e74658fe3", - "743a5825d7b440f8bfd7b1e88af0abc6", - "039848a6742c43328212640e42990961", - "55f850559f754705b7a8d1fe0ee1e8c3", - "33f352bd5a724ef2a71092cf5970c0f2", - "8bd3cbe2ee9b414a80b0789f7b00c9c7", - "ab4055c4bdb14037aa4794739055cc7b", - "69a7e6ddcede47ce85a87ef8d5cf6767", - "18b82ce79e924a188f0f33214c57f493", - "5e880881bc3b494a9e26a3146fc8c774", - "acff26cf14bf4129bde6d386ad38871f", - "b297a6bc484643b4a0176aea55f79870", - "0482c8851cb1488db9aae307295698d5", - "ac9352cd7071440cba14476411b802f2", - "2661a166138a4ed795dafed88d84fb73", - "9cd2d0132d2e4fbe9bc90db60f29e839", - "fddba60bf7bf4744a508f2e843cf00e2", - "8c4b864b61cb476295ac280139679589", - "441b7e9bff434ba28d646f334f65e3c9", - "f8ed8592e944455eb4abe8bcfcdeacbc", - "6004bed837314d4b92b4b8560fc79241", - "3c21bd3d1897498eb75093be30c28c65", - "49c8e448865940828af9af47128d2760", - "11c9f1aad24e4856b89dc13b27cd0985", - "b880a75e5e824e6eb1cb51a51d248243", - "fcb80caa89da4db7ba0f51bce747743c", - "f6bac025e3af4307a3509212b83efd86", - "3011878d75e14b36b8fa450310d7a0d3", - "6b13b046ed444414ae304ca52f005075", - "8e4c4000aa0a4d799dd28bb3fe53dbe8", - "eca3628cbbcc4f3f9c3d2fa14cc4b9eb", - "39373f90245548e1951a43b07752b873", - "b0ee323e3491422cbe59afc9cda70a33", - "36274655be3a4a07b197ed058eab47a8", - "f38a79c0807d4e6ea7fae3368f4900a5" - ] - }, - "id": "7qlcbH234vCj", - "outputId": "cfcb5659-4a32-4a85-b5d8-7a0798f8ede7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0%| | 0.00/685 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Fall 2023/Model building/Llama/SC+QA Training/Dataset/Test Dataset/output_file.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "preds" - ], - "metadata": { - "id": "x7hpkK_567Oe" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from rouge import Rouge" - ], - "metadata": { - "id": "38_h5TrX68u7" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "act_ans = []\n", - "pred_ans = []\n", - "\n", - "for index, row in preds.iterrows():\n", - "\n", - " act_ans.append(row['answer'])\n", - " pred_ans.append(row['predicted'])" - ], - "metadata": { - "id": "uJeU-VAh6_C2" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "hyps, refs = map(list, (pred_ans,act_ans))\n", - "rouge = Rouge()\n", - "\n", - "scores = rouge.get_scores(hyps, refs, avg=True)\n", - "scores" - ], - "metadata": { - "id": "txQqDfpG7A1M" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from tabulate import tabulate" - ], - "metadata": { - "id": "bDTVwYGp7Clr" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Create a list of dictionaries for tabulating the scores\n", - "score_table = [{'Metric': metric, 'Precision': score['p'], 'Recall': score['r'], 'F1-Score': score['f']} for metric, score in scores.items()]\n", - "\n", - "# Print the scores in a tabular format\n", - "print(tabulate(score_table, headers='keys', tablefmt='grid'))" - ], - "metadata": { - "id": "LiS_DuY67EEx" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "rcjIiD1h7FqG" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file