From 3b0cab7eef09bbe777ca43f5187c8e04087bc2ea Mon Sep 17 00:00:00 2001 From: JulianZablonski Date: Thu, 8 Jun 2023 17:34:01 +0200 Subject: [PATCH] zad12 --- dev-0/out.tsv | 21694 ++++++++++++++++++++++--------------------- lab12.ipynb | 443 + model1.bin | Bin 4011299 -> 0 bytes test-A/out.tsv | 23284 ++++++++++++++++++++++++++++++++--------------- 4 files changed, 27488 insertions(+), 17933 deletions(-) create mode 100644 lab12.ipynb delete mode 100644 model1.bin diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 7eadad6..8372ad9 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,10519 +1,11175 @@ -the:0.23660749197006226 a:0.03086458519101143 his:0.016192566603422165 which:0.013625094667077065 :0.3917631506919861 -own:0.01264500804245472 work:0.008636235259473324 passage:0.008503393270075321 way:0.006836367305368185 :0.6695284843444824 -the:0.18709255754947662 a:0.030234193429350853 tho:0.017979390919208527 his:0.01261984370648861 :0.44854918122291565 -and:0.05788029730319977 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"https://localhost:8080/" + }, + "id": "W8-j-5oV0o46", + "outputId": "5cf81efc-7e9b-46a6-d3bd-792a4b4b39b9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting transformers\n", + " Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m105.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n", + "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n", + " Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m33.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + 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collected packages: tokenizers, huggingface-hub, transformers\n", + "Successfully installed huggingface-hub-0.15.1 tokenizers-0.13.3 transformers-4.29.2\n", + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n", + "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n", + "Requirement already satisfied: triton==2.0.0 in 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+ "remote: Wymienianie obiektów: 27, gotowe.\u001b[K\n", + "remote: Zliczanie obiektów: 100% (27/27), gotowe.\u001b[K\n", + "remote: Kompresowanie obiektów: 100% (23/23), gotowe.\u001b[K\n", + "remote: Razem 27 (delty 2), użyte ponownie 17 (delty 0), paczki użyte ponownie 0\u001b[K\n", + "Receiving objects: 100% (27/27), 278.33 MiB | 8.52 MiB/s, done.\n", + "Resolving deltas: 100% (2/2), done.\n" + ] + } + ], + "source": [ + "!git clone --single-branch git://gonito.net/challenging-america-word-gap-prediction -b master" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "uHkXCRs-0iSr" + }, + "outputs": [], + "source": [ + "import torch\n", + "import sys\n", + "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "HyKM4zn41YvQ" + }, + "outputs": [], + "source": [ + "import lzma\n", + "from itertools import islice\n", + "import regex as re\n", + "import sys\n", + "from torchtext.vocab import build_vocab_from_iterator\n", + "from torch import nn\n", + "from torch.utils.data import IterableDataset\n", + "import itertools" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-k8RhlmI06mQ", + "outputId": "e2ef4117-5d5b-40e9-f774-9faba825042c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/challenging-america-word-gap-prediction\n" + ] + } + ], + "source": [ + "%cd /content/challenging-america-word-gap-prediction" + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" + ], + "metadata": { + "id": "PCA7Ank2dnwM" + }, + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n", + "model = GPT2LMHeadModel.from_pretrained(\"gpt2\").to(device)" + ], + "metadata": { + "id": "U0kG_W5AY7uE" + }, + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "F4MXeKLxMQ4N" + }, + "outputs": [], + "source": [ + "def prediction(word: str) -> str:\n", + " left_context =tokenizer.encode(word, return_tensors=\"pt\").to(device)\n", + " out = model(left_context)\n", + " prob_dist=torch.softmax(out[0][-1],dim=1)\n", + " values,index =prob_dist.topk(5)\n", + " token = [] \n", + " for x in index[-1]:\n", + " token.append(tokenizer.decode(x))\n", + " zipped = list(zip(values[-1], token))\n", + " for index, element in enumerate(zipped):\n", + " unk = None\n", + " if '' in element:\n", + " unk = zipped.pop(index)\n", + " zipped.append(('', unk[1]))\n", + " break\n", + " if unk is None:\n", + " zipped[-1] = ('', zipped[-1][1])\n", + " return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "My_1_4L5MMc3" + }, + "outputs": [], + "source": [ + "def create_outputs(folder_name):\n", + " print(f'Creating outputs in {folder_name}')\n", + " with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n", + " with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n", + " for line in fid:\n", + " separated = line.split('\\t')\n", + " prefix = separated[6].replace(r'\\n', ' ').split()[-1]\n", + " output_line = prediction(prefix)\n", + " f.write(output_line + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VQPcLF-OChJ", + "outputId": "7a7408b2-ad26-4041-887e-99a8e9d36d0f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Creating outputs in dev-0\n", + "Creating outputs in test-A\n" + ] + } + ], + "source": [ + "create_outputs('dev-0')\n", + "create_outputs('test-A')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YCGOd41pzfAC" + }, + "outputs": [], + "source": [ + "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n", + "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n", + "def get_words_from_line(line):\n", + " line = line.rstrip()\n", + " yield ''\n", + " for t in line.split():\n", + " yield t\n", + " yield ''\n", + "\n", + "\n", + "def get_word_lines_from_file(file_name):\n", + " with lzma.open(file_name, encoding='utf8', mode=\"rt\") as fh:\n", + " for line in fh:\n", + " pattern = r'\\^\\^|\\n|\\\\|[<>]|[()]'\n", + " line = re.sub(pattern, '', line)\n", + " yield line\n", + "\n", + "for line in get_word_lines_from_file(\"train/in.tsv.xz\"):\n", + " # line = line.strip('\\n')\n", + " # fields = line.split(\"\\t\")\n", + " # print(line)\n", + " left_context = str(line)\n", + " input_ids = tokenizer.encode(left_context, return_tensors=\"pt\")\n", + " # print(input_ids)\n", + " output = model(input_ids)\n", + " # print(output[0].shape())\n", + " prob_dist=torch.softmax(output[0][-1],dim=1)\n", + " values,index =prob_dist.topk(20) \n", + " print(left_context[-100:])\n", + " print(values.size())\n", + " print(index.size())\n", + " break\n", + " for x,indx in zip(values,index):\n", + " for i in range(20):\n", + " token = tokenizer.decode(indx[i])\n", + " print(f'{x[i]} {indx[i]} {token}')\n", + " print('-------------------------')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "01zkM5giNUR3" + }, + "outputs": [], + "source": [ + "\n", + "# line = line.strip('\\n')\n", + "# fields = line.split(\"\\t\")\n", + "# print(line)\n", + "left_context = \"he\"\n", + "input_ids = tokenizer.encode(left_context, return_tensors=\"pt\")\n", + "# print(input_ids)\n", + "output = model(input_ids)\n", + "# print(output[0].shape())\n", + "prob_dist=torch.softmax(output[0][-1],dim=1)\n", + "values,index =prob_dist.topk(5) \n", + "token = []\n", + "for x in index[-1]:\n", + " token.append(tokenizer.decode(x))\n", + " # print(token)\n", + "for x,token in zip(values[-1],token):\n", + " # token = tokenizer.decode(indx)\n", + " print(f'{x} {token}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lDc9Nw40C3dr" + }, + "outputs": [], + "source": [ + "for line in get_word_lines_from_file(\"dev-0/in.tsv.xz\"):\n", + " # line = line.strip('\\n')\n", + " # fields = line.split(\"\\t\")\n", + " # print(line)\n", + " left_context = str(line)\n", + " input_ids = tokenizer.encode(left_context, return_tensors=\"pt\")\n", + " # print(input_ids)\n", + " output = model(input_ids)\n", + " # print(output[0].shape())\n", + " prob_dist=torch.softmax(output[0][-1],dim=1)\n", + " values,index =prob_dist.topk(20) \n", + " print(left_context[-100:])\n", + " # print(values.size())\n", + " # print(index.size())\n", + " # print(values[])\n", + " # break\n", + " for x,indx in zip(values[-1],index[-1]):\n", + " token = tokenizer.decode(indx)\n", + " print(f'{x} {indx} {token}')\n", + " print('-------------------------')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "si7wLC2Tx-kg" + }, + "outputs": [], + "source": [ + "token = tokenizer.decode(256 )\n", + "print(token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lJoE0Cwz0JCM" + }, + "outputs": [], + "source": [ + "top_indices[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tgmT1vG20U_1" + }, + "outputs": [], + "source": [ + "top_probs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U9GVSAZz4SlW" + }, + "outputs": [], + "source": [ + "top =prob_dist.topk(20) \n", + "top_indices = top.indices.tolist()\n", + "top_probs = top.values.tolist()\n", + "top_words = tokenizer.decode(top_indices)\n", + "print(top_words,'\\n',top_indices,'\\n',top_probs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8_WSZ_v99xSH" + }, + "outputs": [], + "source": [ + "print(index[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OAiJNMNMwNNg" + }, + "outputs": [], + "source": [ + "print(prob_dist.topk(2)[0].size())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PIUjH8-ow1y9" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/model1.bin b/model1.bin deleted file mode 100644 index 1fb1f0491758883150f17f8767db6d908400dd82..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4011299 zcmZ_#30O_x*FTIGY1BYzj-pALkkYX4l_VvJq)AGYL`fPDQA(4NQqd$yNt8w!&c4@C 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