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@NarineK solution for BERT interpretation in https://github.com/pytorch/captum/issues/311#issuecomment-612460705
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "orig_nbformat": 2, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "npconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": 3, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "colab": { | |
| "name": "Copy of Interpretation.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/davidefiocco/47137f6eb7e3351c9bac4580c2ccc9d4/copy-of-interpretation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "UFESEuEgbUDD", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Interpretation of BertForSequenceClassification in captum\n", | |
| "\n", | |
| "In this notebook we use Captum to interpret a BERT sentiment classifier finetuned on the imdb dataset https://huggingface.co/lvwerra/bert-imdb " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "EJ51JAxHbghp", | |
| "colab_type": "code", | |
| "outputId": "856672a7-298c-4f26-a108-338c7027aa47", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 884 | |
| } | |
| }, | |
| "source": [ | |
| "# Install dependencies\n", | |
| "!pip install transformers\n", | |
| "!pip install captum" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Collecting transformers\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a3/78/92cedda05552398352ed9784908b834ee32a0bd071a9b32de287327370b7/transformers-2.8.0-py3-none-any.whl (563kB)\n", | |
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| "Collecting sacremoses\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a6/b4/7a41d630547a4afd58143597d5a49e07bfd4c42914d8335b2a5657efc14b/sacremoses-0.0.38.tar.gz (860kB)\n", | |
| "\u001b[K |████████████████████████████████| 870kB 25.0MB/s \n", | |
| "\u001b[?25hCollecting sentencepiece\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/74/f4/2d5214cbf13d06e7cb2c20d84115ca25b53ea76fa1f0ade0e3c9749de214/sentencepiece-0.1.85-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n", | |
| "\u001b[K |████████████████████████████████| 1.0MB 29.2MB/s \n", | |
| "\u001b[?25hCollecting tokenizers==0.5.2\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/d1/3f/73c881ea4723e43c1e9acf317cf407fab3a278daab3a69c98dcac511c04f/tokenizers-0.5.2-cp36-cp36m-manylinux1_x86_64.whl (3.7MB)\n", | |
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| "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.16.0,>=1.15.34->boto3->transformers) (0.15.2)\n", | |
| "Building wheels for collected packages: sacremoses\n", | |
| " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for sacremoses: filename=sacremoses-0.0.38-cp36-none-any.whl size=884628 sha256=77a6b4aabd517c57817a0b235589cda947e4af206a8785014c0761c26c3a065e\n", | |
| " Stored in directory: /root/.cache/pip/wheels/6d/ec/1a/21b8912e35e02741306f35f66c785f3afe94de754a0eaf1422\n", | |
| "Successfully built sacremoses\n", | |
| "Installing collected packages: sacremoses, sentencepiece, tokenizers, transformers\n", | |
| "Successfully installed sacremoses-0.0.38 sentencepiece-0.1.85 tokenizers-0.5.2 transformers-2.8.0\n", | |
| "Collecting captum\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/42/de/c018e206d463d9975444c28b0a4f103c9ca4b2faedf943df727e402a1a1e/captum-0.2.0-py3-none-any.whl (1.4MB)\n", | |
| "\u001b[K |████████████████████████████████| 1.4MB 6.9MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from captum) (3.2.1)\n", | |
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| "Requirement already satisfied: torch>=1.2 in /usr/local/lib/python3.6/dist-packages (from captum) (1.4.0)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->captum) (1.2.0)\n", | |
| "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->captum) (2.8.1)\n", | |
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| "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->captum) (2.4.6)\n", | |
| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->captum) (1.12.0)\n", | |
| "Installing collected packages: captum\n", | |
| "Successfully installed captum-0.2.0\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "CS9Kaz8ubUDG", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "from transformers import BertTokenizer, BertForSequenceClassification, BertConfig\n", | |
| "from captum.attr import visualization as viz\n", | |
| "from captum.attr import IntegratedGradients, LayerConductance, LayerIntegratedGradients\n", | |
| "from captum.attr import configure_interpretable_embedding_layer, remove_interpretable_embedding_layer\n", | |
| "import torch\n", | |
| "import matplotlib.pyplot as plt" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "P1yl1gdvbUDS", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3U5XDt1Gb73t", | |
| "colab_type": "code", | |
| "outputId": "12031166-fbed-47f0-e081-61c1f4f447d7", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| } | |
| }, | |
| "source": [ | |
| "# Get model and config files from https://huggingface.co/lvwerra/bert-imdb\n", | |
| "!wget -P ./model https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/config.json\n", | |
| "!wget -P ./model https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/pytorch_model.bin\n", | |
| "!wget -P ./model https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/special_tokens_map.json\n", | |
| "!wget -P ./model https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/tokenizer_config.json\n", | |
| "!wget -P ./model https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/training_args.bin\n", | |
| "!wget -P ./model https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/vocab.txt" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "--2020-04-06 19:08:32-- https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/config.json\n", | |
| "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.184.165\n", | |
| "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.184.165|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 1220 (1.2K) [application/json]\n", | |
| "Saving to: ‘./model/config.json’\n", | |
| "\n", | |
| "\rconfig.json 0%[ ] 0 --.-KB/s \rconfig.json 100%[===================>] 1.19K --.-KB/s in 0s \n", | |
| "\n", | |
| "2020-04-06 19:08:32 (68.1 MB/s) - ‘./model/config.json’ saved [1220/1220]\n", | |
| "\n", | |
| "--2020-04-06 19:08:34-- https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/pytorch_model.bin\n", | |
| "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.41.190\n", | |
| "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.41.190|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 1334420863 (1.2G) [application/octet-stream]\n", | |
| "Saving to: ‘./model/pytorch_model.bin’\n", | |
| "\n", | |
| "pytorch_model.bin 100%[===================>] 1.24G 28.9MB/s in 44s \n", | |
| "\n", | |
| "2020-04-06 19:09:18 (29.1 MB/s) - ‘./model/pytorch_model.bin’ saved [1334420863/1334420863]\n", | |
| "\n", | |
| "--2020-04-06 19:09:20-- https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/special_tokens_map.json\n", | |
| "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.187.85\n", | |
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| "Saving to: ‘./model/special_tokens_map.json’\n", | |
| "\n", | |
| "special_tokens_map. 100%[===================>] 112 --.-KB/s in 0s \n", | |
| "\n", | |
| "2020-04-06 19:09:20 (2.09 MB/s) - ‘./model/special_tokens_map.json’ saved [112/112]\n", | |
| "\n", | |
| "--2020-04-06 19:09:21-- https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/tokenizer_config.json\n", | |
| "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.29.14\n", | |
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| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 40 [application/json]\n", | |
| "Saving to: ‘./model/tokenizer_config.json’\n", | |
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| "\n", | |
| "--2020-04-06 19:09:23-- https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/training_args.bin\n", | |
| "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.76.254\n", | |
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| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 1449 (1.4K) [application/octet-stream]\n", | |
| "Saving to: ‘./model/training_args.bin’\n", | |
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| "\n", | |
| "--2020-04-06 19:09:25-- https://s3.amazonaws.com/models.huggingface.co/bert/lvwerra/bert-imdb/vocab.txt\n", | |
| "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.80.75\n", | |
| "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.80.75|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 213450 (208K) [text/plain]\n", | |
| "Saving to: ‘./model/vocab.txt’\n", | |
| "\n", | |
| "vocab.txt 100%[===================>] 208.45K 845KB/s in 0.2s \n", | |
| "\n", | |
| "2020-04-06 19:09:25 (845 KB/s) - ‘./model/vocab.txt’ saved [213450/213450]\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "X-nyyq_tbUDa", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# load model\n", | |
| "model = BertForSequenceClassification.from_pretrained('./model')\n", | |
| "model.to(device)\n", | |
| "model.eval()\n", | |
| "model.zero_grad()\n", | |
| "\n", | |
| "# load tokenizer\n", | |
| "tokenizer = BertTokenizer.from_pretrained('./model')" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JUMsvUOTbUDi", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def predict(inputs):\n", | |
| " return model(inputs)[0]" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SIbauwGbbUDo", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "ref_token_id = tokenizer.pad_token_id # A token used for generating token reference\n", | |
| "sep_token_id = tokenizer.sep_token_id # A token used as a separator between question and text and it is also added to the end of the text.\n", | |
| "cls_token_id = tokenizer.cls_token_id # A token used for prepending to the concatenated question-text word sequence" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "mcnTCNUFbUD1", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def construct_input_ref_pair(text, ref_token_id, sep_token_id, cls_token_id):\n", | |
| "\n", | |
| " text_ids = tokenizer.encode(text, add_special_tokens=False)\n", | |
| " # construct input token ids\n", | |
| " input_ids = [cls_token_id] + text_ids + [sep_token_id]\n", | |
| " # construct reference token ids \n", | |
| " ref_input_ids = [cls_token_id] + [ref_token_id] * len(text_ids) + [sep_token_id]\n", | |
| "\n", | |
| " return torch.tensor([input_ids], device=device), torch.tensor([ref_input_ids], device=device), len(text_ids)\n", | |
| "\n", | |
| "def construct_input_ref_token_type_pair(input_ids, sep_ind=0):\n", | |
| " seq_len = input_ids.size(1)\n", | |
| " token_type_ids = torch.tensor([[0 if i <= sep_ind else 1 for i in range(seq_len)]], device=device)\n", | |
| " ref_token_type_ids = torch.zeros_like(token_type_ids, device=device)# * -1\n", | |
| " return token_type_ids, ref_token_type_ids\n", | |
| "\n", | |
| "def construct_input_ref_pos_id_pair(input_ids):\n", | |
| " seq_length = input_ids.size(1)\n", | |
| " position_ids = torch.arange(seq_length, dtype=torch.long, device=device)\n", | |
| " # we could potentially also use random permutation with `torch.randperm(seq_length, device=device)`\n", | |
| " ref_position_ids = torch.zeros(seq_length, dtype=torch.long, device=device)\n", | |
| "\n", | |
| " position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n", | |
| " ref_position_ids = ref_position_ids.unsqueeze(0).expand_as(input_ids)\n", | |
| " return position_ids, ref_position_ids\n", | |
| " \n", | |
| "def construct_attention_mask(input_ids):\n", | |
| " return torch.ones_like(input_ids)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vhasPia4bUD8", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def custom_forward(inputs):\n", | |
| " preds = predict(inputs)\n", | |
| " return torch.softmax(preds, dim = 1)[0][1].unsqueeze(-1)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pGwkb1vAbUEA", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "lig = LayerIntegratedGradients(custom_forward, model.bert.embeddings)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "EQlVDaISbUEF", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# One can test a couple of examples and check that the sentiment classifier is behaving\n", | |
| "text = \"The movie was one of those amazing movies\"#\"The movie was one of those amazing movies you can not forget\"\n", | |
| "#text = \"The movie was one of those crappy movies you can't forget.\"" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BtoFctjVbUEM", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "input_ids, ref_input_ids, sep_id = construct_input_ref_pair(text, ref_token_id, sep_token_id, cls_token_id)\n", | |
| "token_type_ids, ref_token_type_ids = construct_input_ref_token_type_pair(input_ids, sep_id)\n", | |
| "position_ids, ref_position_ids = construct_input_ref_pos_id_pair(input_ids)\n", | |
| "attention_mask = construct_attention_mask(input_ids)\n", | |
| "\n", | |
| "indices = input_ids[0].detach().tolist()\n", | |
| "all_tokens = tokenizer.convert_ids_to_tokens(indices)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "T4vlqBBrbUEY", | |
| "colab_type": "code", | |
| "outputId": "f44b69f0-06f6-4202-aa16-99b24c1dc8b0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "# Check predict output\n", | |
| "predict(input_ids)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "tensor([[-3.1333, 3.6520]], device='cuda:0', grad_fn=<AddmmBackward>)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 26 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "gcuf6-v1v952", | |
| "colab_type": "code", | |
| "outputId": "86097a72-6c8d-4e18-e8b2-0e115bca3945", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "pred = predict(input_ids)\n", | |
| "torch.softmax(pred, dim = 1)\n" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "tensor([[0.0011, 0.9989]], device='cuda:0', grad_fn=<SoftmaxBackward>)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 27 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "wpNkwy6_bUEd", | |
| "colab_type": "code", | |
| "outputId": "491b4b9d-ea96-492c-b0e6-87070bad077c", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "# Check output of custom_forward\n", | |
| "custom_forward(input_ids)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "tensor([0.9989], device='cuda:0', grad_fn=<UnsqueezeBackward0>)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 28 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "YAzBqQlpbUEk", | |
| "colab_type": "code", | |
| "outputId": "d96b1d65-38ac-4fd4-970a-86570893246c", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| } | |
| }, | |
| "source": [ | |
| "attributions, delta = lig.attribute(inputs=input_ids,\n", | |
| " baselines=ref_input_ids,\n", | |
| " n_steps=700,\n", | |
| " internal_batch_size=3,\n", | |
| " return_convergence_delta=True)\n", | |
| "delta" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "tensor([-0.5689], device='cuda:0')" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 29 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dU8SRQFybUEo", | |
| "colab_type": "code", | |
| "outputId": "526898f4-3b6d-4946-c9aa-71e402c84753", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| } | |
| }, | |
| "source": [ | |
| "score = predict(input_ids)\n", | |
| "\n", | |
| "print('Sentence: ', text)\n", | |
| "print('Sentiment: ' + str(torch.argmax(score[0]).cpu().numpy()) + \\\n", | |
| " ', Probability positive: ' + str(torch.softmax(score, dim = 1)[0][1].cpu().detach().numpy()))" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Sentence: The movie was one of those amazing movies\n", | |
| "Sentiment: 1, Probability positive: 0.9988709\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Hq8R_ZYubUEu", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def summarize_attributions(attributions):\n", | |
| " attributions = attributions.sum(dim=-1).squeeze(0)\n", | |
| " attributions = attributions / torch.norm(attributions)\n", | |
| " return attributions" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "3q7xXwRrbUEx", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "attributions_sum = summarize_attributions(attributions)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "0ZF0RmZ4bUE1", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# storing couple samples in an array for visualization purposes\n", | |
| "score_vis = viz.VisualizationDataRecord(attributions_sum,\n", | |
| " torch.softmax(score, dim = 1)[0][1],\n", | |
| " torch.argmax(torch.softmax(score, dim = 0)[0]),\n", | |
| " 1,\n", | |
| " text,\n", | |
| " attributions_sum.sum(), \n", | |
| " all_tokens,\n", | |
| " delta)\n" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-gAojuO6ody0", | |
| "colab_type": "code", | |
| "outputId": "03a72a37-2e04-495e-a1e0-5a191e34733e", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 87 | |
| } | |
| }, | |
| "source": [ | |
| "print('\\033[1m', 'Visualization For Score', '\\033[0m')\n", | |
| "viz.visualize_text([score_vis])" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[1m Visualization For Score \u001b[0m\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<table width: 100%><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>1 (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>The movie was one of those amazing movies</b></text></td><td><text style=\"padding-right:2em\"><b>0.26</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [CLS] </font></mark><mark style=\"background-color: hsl(0, 75%, 91%); opacity:1.0; line-height:1.75\"><font color=\"black\"> The </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0; line-height:1.75\"><font color=\"black\"> movie </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0; line-height:1.75\"><font color=\"black\"> was </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0; line-height:1.75\"><font color=\"black\"> one </font></mark><mark style=\"background-color: hsl(120, 75%, 89%); opacity:1.0; line-height:1.75\"><font color=\"black\"> of </font></mark><mark style=\"background-color: hsl(0, 75%, 87%); opacity:1.0; line-height:1.75\"><font color=\"black\"> those </font></mark><mark style=\"background-color: hsl(120, 75%, 60%); opacity:1.0; line-height:1.75\"><font color=\"black\"> amazing </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0; line-height:1.75\"><font color=\"black\"> movies </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0; line-height:1.75\"><font color=\"black\"> [SEP] </font></mark></td><tr></table>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ItXD4N9FogZu", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "The visualization is clearly meaningless! :(\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "N-njUmIyzzGI", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "torch.softmax(score, dim = 1)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "leiLQGsVmISM", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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@NarineK solution in pytorch/captum#311 (comment)