diff --git "a/notebooks/named_entity_recognition_dev.ipynb" "b/notebooks/named_entity_recognition_dev.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j9x-5xxtgJYA"
+ },
+ "source": [
+ "# [Token classification (PyTorch)](https://huggingface.co/learn/nlp-course/en/chapter7/2?fw=pt)\n",
+ "\n",
+ "* The Dataset structure contains rows of dictionary where each dictionary correspond to a data point consisting of features and coresponding values\n",
+ "* `Dataset` object also has a number of methods and attributes\n",
+ "* `DataCollatorForTokenClassification` pads with `-100`\n",
+ "* The traditional framework used to evaluate token classification prediction is [`seqeval`](https://huggingface.co/spaces/evaluate-metric/seqeval)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### Main steps to prepare custom training loop\n",
+ "1. Prepare the dataloaders\n",
+ "2. Prepare the model -> model initialisation and passing in 1d2label and label2id\n",
+ "3. Prepare the optimizer\n",
+ "4. Pass all to accelerate.prepare()"
+ ],
+ "metadata": {
+ "id": "4xzr2_FURfQP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "53wt9QqZgJYC"
+ },
+ "source": [
+ "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EO3w1a41gJYC",
+ "outputId": "3e1b07df-ceb4-4e4a-8aa4-dfc74fc81530"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting datasets\n",
+ " Downloading datasets-2.16.0-py3-none-any.whl (507 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hCollecting evaluate\n",
+ " Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "Collecting pyarrow-hotfix (from datasets)\n",
+ " Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
+ "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
+ " Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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+ "\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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+ "Collecting responses<0.19 (from evaluate)\n",
+ " Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2023.6.3)\n",
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+ "Collecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
+ " Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+ "Installing collected packages: sentencepiece, pyarrow-hotfix, dill, responses, multiprocess, datasets, evaluate\n",
+ "Successfully installed datasets-2.16.0 dill-0.3.7 evaluate-0.4.1 multiprocess-0.70.15 pyarrow-hotfix-0.6 responses-0.18.0 sentencepiece-0.1.99\n",
+ "Collecting accelerate\n",
+ " Downloading accelerate-0.25.0-py3-none-any.whl (265 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
+ "Installing collected packages: accelerate\n",
+ "Successfully installed accelerate-0.25.0\n",
+ "\u001b[31mERROR: torch_xla-1.9-cp37-cp37m-linux_x86_64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\n",
+ "Reading package lists... Done\n",
+ "Building dependency tree... Done\n",
+ "Reading state information... Done\n",
+ "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n",
+ "0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install datasets evaluate transformers[sentencepiece]\n",
+ "!pip install accelerate\n",
+ "# To run the training on TPU, you will need to uncomment the following line:\n",
+ "!pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
+ "!apt install git-lfs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uyOjlt8igJYD"
+ },
+ "source": [
+ "You will need to setup git, adapt your email and name in the following cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "GS9z71W1gJYD"
+ },
+ "outputs": [],
+ "source": [
+ "!git config --global user.email \"koakande@gmail.com\"\n",
+ "!git config --global user.name \"koakande\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tk9JVa10gJYE"
+ },
+ "source": [
+ "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 331,
+ "referenced_widgets": [
+ "b09ce087c3134d64839edbc4a568f742",
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+ ]
+ },
+ "id": "2_b6ALK3gJYE",
+ "outputId": "3adce3a2-adb4-4a5b-9b8d-2927776678fd"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
multiple labels due from multple examples\n",
+ " all_labels = examples[\"ner_tags\"]\n",
+ " new_labels = []\n",
+ " # iterate over each example and expand the label\n",
+ " for i, labels in enumerate(all_labels):\n",
+ " word_ids = tokenized_inputs.word_ids(i)\n",
+ " new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
+ " if i < 2:\n",
+ " print(new_labels)\n",
+ "\n",
+ " tokenized_inputs[\"labels\"] = new_labels\n",
+ " return tokenized_inputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 955,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "N4YFu7EkgJYH",
+ "outputId": "936fbc15-73b7-4abc-cb8b-9528101589ba"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/14041 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "[[-100, 1, 2, 2, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 1, 2, 2, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 3, 4, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, -100]]\n",
+ "[[-100, 3, 0, 3, 4, 4, 0, -100]]\n",
+ "[[-100, 3, 0, 3, 4, 4, 0, -100], [-100, 3, 0, 3, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 5, 6, 6, 6, 7, 8, 8, 0, 0, 0, 0, 0, 5, 0, -100]]\n",
+ "[[-100, 5, 6, 6, 6, 7, 8, 8, 0, 0, 0, 0, 0, 5, 0, -100], [-100, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, -100]]\n",
+ "[[-100, 3, 4, 4, 4, 4, -100]]\n",
+ "[[-100, 3, 4, 4, 4, 4, -100], [-100, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 1, 2, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 1, 2, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 1, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 5, 0, 0, 7, 8, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 5, 0, 0, 7, 8, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, -100], [-100, 0, 0, 0, 5, 6, 6, 6, 0, 5, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 1, 2, 0, -100]]\n",
+ "[[-100, 5, 6, 6, 6, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 5, 6, 6, 6, 0, 0, 0, 0, 0, -100], [-100, 5, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 1, 2, 2, 0, 5, 0, 3, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 1, 2, 2, 0, 5, 0, 3, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 1, 2, 2, 2, 2, 0, 5, 0, 3, 0, 0, 0, 0, 0, 0, -100]]\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/3250 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5b61bcf5884943f2b4a048795f3e45a5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[[-100, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 5, 6, 6, 6, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 5, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 7, 8, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 3, 4, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 3, 4, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 3, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 7, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 7, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 5, 6, 6, 6, 0, 0, 0, 0, 0, -100]]\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/3453 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e4b92641775a4d56aaa75cc19e3bdee0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[[-100, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 1, 2, 2, 2, 2, 2, -100]]\n",
+ "[[-100, 5, 6, 6, 6, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 5, 6, 6, 6, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 3, 4, 0, 0, 0, 0, 0, 0, 0, -100]]\n",
+ "[[-100, 3, 4, 0, 0, 0, 0, 0, 0, 0, -100], [-100, 3, 0, 0, 0, 0, 0, 0, 0, -100]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# only return the tokenized fields and labels\n",
+ "tokenized_datasets = raw_datasets.map(\n",
+ " tokenize_and_align_labels,\n",
+ " batched=True,\n",
+ " remove_columns=raw_datasets[\"train\"].column_names,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "dnbe6lLIgJYH"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import DataCollatorForTokenClassification\n",
+ "\n",
+ "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eXUxcdXkgJYH",
+ "outputId": "3697ae84-6e80-40d2-f0e5-ab899b66c620"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "tensor([[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100],\n",
+ " [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
+ "print(batch[\"labels\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "4O2drn19gJYH",
+ "outputId": "c471c2db-4de3-4695-8168-dd3d32542639"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
+ "[-100, 1, 2, -100]\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(2):\n",
+ " print(tokenized_datasets[\"train\"][i][\"labels\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "* Comparing the above outputs shows that BERT/HuggingFace AutoTokenizerForTokenClassification pads with `-100`."
+ ],
+ "metadata": {
+ "id": "1uLMadk4rOxF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Rskjs95MgJYH",
+ "outputId": "29134926-94c8-4f56-a989-9eddc0ea5a73"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting seqeval\n",
+ " Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m600.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.23.5)\n",
+ "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.2.2)\n",
+ "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.11.4)\n",
+ "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.3.2)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.2.0)\n",
+ "Building wheels for collected packages: seqeval\n",
+ " Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16162 sha256=458ae6a84124c9dafcda81750e52050bbfd1edd750ebd40049b323f9b3dae547\n",
+ " Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
+ "Successfully built seqeval\n",
+ "Installing collected packages: seqeval\n",
+ "Successfully installed seqeval-1.2.2\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install seqeval"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "xZbAih33gJYH",
+ "outputId": "322d4c78-fe56-409a-a520-f148dd5b5521",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "1a81a79dc52641a4ab1b57890c97e85a",
+ "ea937b7c1edb47ecbe70c9df27a3c3a5",
+ "f4563797c3cf4e1db6063bcdcc5a4335",
+ "42ff9ba7fca24986adbecca6ed17ba78",
+ "a4f697f1d4a6454fb076a19165e399fc",
+ "43138242ad4a4c1897130b8852e4ded9",
+ "0abac9af4caa44ef87a59f3b7b2a4879",
+ "8aac758dc81c4f228c35a467d1458ca4",
+ "d28f69cc0d064fd1a5c7dd679a438ee8",
+ "5b8adfe7fc704e84aa8c6939f218318a",
+ "963af7b5f84c41039fb0f971b8468581"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading builder script: 0%| | 0.00/6.34k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1a81a79dc52641a4ab1b57890c97e85a"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "# the metric seqeval is loaded via evaluate\n",
+ "import evaluate\n",
+ "\n",
+ "metric = evaluate.load(\"seqeval\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "label_names\n",
+ "labels"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "45I3sNo6xrMf",
+ "outputId": "2e351d19-3781-46a8-abc9-992dc9c02774"
+ },
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 29
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "onIAOm3bgJYH",
+ "outputId": "77886ba4-035b-40f9-977b-8186856596b6"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 30
+ }
+ ],
+ "source": [
+ "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
+ "labels = [label_names[i] for i in labels]\n",
+ "labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "LdYiLnJQgJYI",
+ "outputId": "90f6245b-7935-4f16-dd07-ba5e1916da70"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'MISC': {'precision': 1.0,\n",
+ " 'recall': 0.5,\n",
+ " 'f1': 0.6666666666666666,\n",
+ " 'number': 2},\n",
+ " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
+ " 'overall_precision': 1.0,\n",
+ " 'overall_recall': 0.6666666666666666,\n",
+ " 'overall_f1': 0.8,\n",
+ " 'overall_accuracy': 0.8888888888888888}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 31
+ }
+ ],
+ "source": [
+ "predictions = labels.copy()\n",
+ "predictions[2] = \"O\"\n",
+ "metric.compute(predictions=[predictions], references=[labels])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#### Model output\n",
+ "* Model logits matrix size is token_num by label_num. That is the number of tokens in input text by num of possible labels.\n",
+ "* This gives probability of each token being/belonging in any of the labelled entities\n",
+ "* np.argmax will then return the index of the label with the highest logits titled predictions\n",
+ "* compute metrics takes in a model output for an example and compute the metrics"
+ ],
+ "metadata": {
+ "id": "XeGRSXqq5fd2"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "id": "CHyOiV5_gJYI"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "def compute_metrics(eval_preds):\n",
+ " # obtain model output logits and corresponding labels\n",
+ " logits, labels = eval_preds\n",
+ " # extract index of max logit for each token\n",
+ " predictions = np.argmax(logits, axis=-1)\n",
+ "\n",
+ " # Remove ignored index (special tokens) and convert to labels\n",
+ " true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
+ " true_predictions = [\n",
+ " [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ " # compute metric per example/sample\n",
+ " all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
+ " return {\n",
+ " \"precision\": all_metrics[\"overall_precision\"],\n",
+ " \"recall\": all_metrics[\"overall_recall\"],\n",
+ " \"f1\": all_metrics[\"overall_f1\"],\n",
+ " \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "id": "lnUcmDPJgJYI"
+ },
+ "outputs": [],
+ "source": [
+ "id2label = {i: label for i, label in enumerate(label_names)}\n",
+ "label2id = {v: k for k, v in id2label.items()}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "label2id"
+ ],
+ "metadata": {
+ "id": "givK4zGXB1V1",
+ "outputId": "54a7f14a-6b96-4f9b-8a4b-a4b41627dbac",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 34,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'O': 0,\n",
+ " 'B-PER': 1,\n",
+ " 'I-PER': 2,\n",
+ " 'B-ORG': 3,\n",
+ " 'I-ORG': 4,\n",
+ " 'B-LOC': 5,\n",
+ " 'I-LOC': 6,\n",
+ " 'B-MISC': 7,\n",
+ " 'I-MISC': 8}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105,
+ "referenced_widgets": [
+ "8e2d83ee2e7341a6ad50ca7b03e1bf71",
+ "529dc17da2364bbab9038628e636969c",
+ "6dd60fed346e4cb6b0d10f6a11b48f3b",
+ "7f1570af102f4a2e851bb7077622803c",
+ "e84694a0d79a4a2b9a3ec8915f419910",
+ "89a3c98b34c8469180e57283cb68dca6",
+ "5838613ab1c04a549b8bd6c17be08a3b",
+ "0b9ed9667d624b3592edd694c856b86b",
+ "c48512cedee14082a0aee3147c616feb",
+ "3b3469c8772347549c03f1ad07117d4f",
+ "f36668a20d314a8e81d3f05dc3873f0b"
+ ]
+ },
+ "id": "urXgRGxQgJYI",
+ "outputId": "f534be3b-f786-4595-fdf0-44f4b8c4bea3"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/436M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8e2d83ee2e7341a6ad50ca7b03e1bf71"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import AutoModelForTokenClassification\n",
+ "\n",
+ "model = AutoModelForTokenClassification.from_pretrained(\n",
+ " model_checkpoint,\n",
+ " id2label=id2label,\n",
+ " label2id=label2id,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "B5b8hNuQgJYI",
+ "outputId": "d0220e33-4ca0-4e3f-841b-2e6a31510d77"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "9"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 36
+ }
+ ],
+ "source": [
+ "model.config.num_labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 145,
+ "referenced_widgets": [
+ "bb33ae7c92f54ad88aa273a436d7e99f",
+ "6b80509174114a1dba02ad656ba736c1",
+ "718e09129d8a4864a0b13169585e8108",
+ "03b6530f981045269eaea9ccd88fcfcf",
+ "c7fb80b5599847eaa9a0a1625840f0c9",
+ "bc4d1a16a9134baa95da96b6164b2a94",
+ "95095d5a0e2c48598a82b45a4caddfc1",
+ "ab1d05ae22f3413da5163a0ef01dc678",
+ "e456ac1074824b30923fd3f79afd8c86",
+ "a4b92363db1d42a797fb83dbd4c899f2",
+ "3255cb6394c54ecb92f62cb15f6ac163",
+ "5ffc01df6da54b88a5d131606382afad",
+ "a7c94dea85a541f7a580c6fe40ea04fa",
+ "dd2571ba70ca42c3a4f3ad043c47da73",
+ "05e6c60cf2da495da9549b3a20de8d09",
+ "b804b75f1cc44600ac448a2b5c6992f4",
+ "12eb6dd0b4b143ecb8b776bb25108ad3",
+ "1e359b191d5c408baef2c4c0bf7535e1",
+ "a54cac00fc87441bb57a0362d2fef58f",
+ "c9b52d7ffc054540a0c1f5bc00436a4a",
+ "5a33e3027b774bf58fc01859a6d2da2b",
+ "d016a76eb2164ab99e318cb7c1d8e7e5",
+ "c16e9afa61ba42f08c168b1754af6fbe",
+ "a4a98b90989e4ab2acb7da977b9083c7",
+ "aadb0290f7fc441e81e2c721dc70b9ee",
+ "d473fd173542467c943e9f9936c808c3",
+ "c328392c5c5645fdbbb346a14b4e275f",
+ "49b82980fbbd44f3840059fbb1543371",
+ "3c3c6c07694f4a9bb4632856bfe0d867",
+ "b51fbaa6812740f8903a7a326e9eae4f",
+ "8277b0db34f945ccb2966401cc1ea93f",
+ "ebb37a6594fd44fabbc3fef36db37888"
+ ]
+ },
+ "id": "9D7Z7q2LgJYI",
+ "outputId": "c514af55-b9dc-4fa0-fbd9-629a0eab6983"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m trainer = Trainer(\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_hf_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 537\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_save\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36minit_hf_repo\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 3476\u001b[0m \u001b[0mrepo_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3478\u001b[0;31m \u001b[0mrepo_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_private_repo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3479\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrepo_url\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3480\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_in_progress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m 2798\u001b[0m \u001b[0;31m# See https://github.com/huggingface/huggingface_hub/pull/733/files#r820604472\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2799\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"lfsmultipartthresh\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lfsmultipartthresh\u001b[0m \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_hf_headers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_write_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2802\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36m_build_hf_headers\u001b[0;34m(self, token, is_write_action, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m 6941\u001b[0m \u001b[0;31m# Cannot do `token = token or self.token` as token can be `False`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6942\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6943\u001b[0;31m return build_hf_headers(\n\u001b[0m\u001b[1;32m 6944\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6945\u001b[0m \u001b[0mis_write_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_write_action\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_headers.py\u001b[0m in \u001b[0;36mbuild_hf_headers\u001b[0;34m(token, is_write_action, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;31m# Get auth token to send\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0mtoken_to_send\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_token_to_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0m_validate_token_to_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken_to_send\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_write_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_write_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;31m# Combine headers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_headers.py\u001b[0m in \u001b[0;36m_validate_token_to_send\u001b[0;34m(token, is_write_action)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_write_action\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;34m\"Token is required (write-access action) but no token found. You need\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;34m\" to provide a token or be logged in to Hugging Face with\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: Token is required (write-access action) but no token found. You need to provide a token or be logged in to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens."
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import Trainer\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=args,\n",
+ " train_dataset=tokenized_datasets[\"train\"],\n",
+ " eval_dataset=tokenized_datasets[\"validation\"],\n",
+ " data_collator=data_collator,\n",
+ " compute_metrics=compute_metrics,\n",
+ " tokenizer=tokenizer,\n",
+ ")\n",
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YkpIZ9Z0gJYI"
+ },
+ "outputs": [],
+ "source": [
+ "trainer.push_to_hub(commit_message=\"Training complete\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "id": "LjYQCQeigJYI"
+ },
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import DataLoader\n",
+ "\n",
+ "train_dataloader = DataLoader(\n",
+ " tokenized_datasets[\"train\"],\n",
+ " shuffle=True,\n",
+ " collate_fn=data_collator,\n",
+ " batch_size=8,\n",
+ ")\n",
+ "eval_dataloader = DataLoader(\n",
+ " tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "id": "R687bCK5gJYI",
+ "outputId": "896f7ed1-7179-4ef5-fedf-3b571a67d6ea",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = AutoModelForTokenClassification.from_pretrained(\n",
+ " model_checkpoint,\n",
+ " id2label=id2label,\n",
+ " label2id=label2id,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "id": "YW2dXicYgJYN"
+ },
+ "outputs": [],
+ "source": [
+ "from torch.optim import AdamW\n",
+ "\n",
+ "optimizer = AdamW(model.parameters(), lr=2e-5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "id": "3dpCXwGPgJYO"
+ },
+ "outputs": [],
+ "source": [
+ "from accelerate import Accelerator\n",
+ "\n",
+ "accelerator = Accelerator()\n",
+ "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
+ " model, optimizer, train_dataloader, eval_dataloader\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "id": "iS_haGsHgJYO"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import get_scheduler\n",
+ "\n",
+ "num_train_epochs = 3\n",
+ "num_update_steps_per_epoch = len(train_dataloader)\n",
+ "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
+ "\n",
+ "lr_scheduler = get_scheduler(\n",
+ " \"linear\",\n",
+ " optimizer=optimizer,\n",
+ " num_warmup_steps=0,\n",
+ " num_training_steps=num_training_steps,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "id": "fEYIaqhNgJYO",
+ "outputId": "7bbf6ea8-5a40-4bd4-d565-b90ddf182166",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'koakande/bert-finetuned-ner-accelerate'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 45
+ }
+ ],
+ "source": [
+ "from huggingface_hub import Repository, get_full_repo_name\n",
+ "\n",
+ "model_name = \"bert-finetuned-ner-accelerate\"\n",
+ "repo_name = get_full_repo_name(model_name)\n",
+ "repo_name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "id": "Qd8YB-6ygJYO",
+ "outputId": "f25a3b5f-da7a-4d89-bc1e-981da2a1da6c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n",
+ "For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n",
+ " warnings.warn(warning_message, FutureWarning)\n",
+ "Cloning https://huggingface.co/koakande/bert-finetuned-ner-accelerate into local empty directory.\n",
+ "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/koakande/bert-finetuned-ner-accelerate into local empty directory.\n"
+ ]
+ }
+ ],
+ "source": [
+ "output_dir = \"bert-finetuned-ner-accelerate\"\n",
+ "repo = Repository(output_dir, clone_from=repo_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "iQEV4cF3gJYO"
+ },
+ "outputs": [],
+ "source": [
+ "def postprocess(predictions, labels):\n",
+ " predictions = predictions.detach().cpu().clone().numpy()\n",
+ " labels = labels.detach().cpu().clone().numpy()\n",
+ "\n",
+ " # Remove ignored index (special tokens) and convert to labels\n",
+ " true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
+ " true_predictions = [\n",
+ " [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ " return true_labels, true_predictions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "id": "qgmZhuaNgJYO",
+ "outputId": "c27ec276-7215-450f-8560-2302475e992a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 103,
+ "referenced_widgets": [
+ "dd5e2c234b5d4cd8b5b79c9adf789799",
+ "7a88edbae055466d981133ae27d2606f",
+ "60b81ee439c64f0185bd0ac5a38962a5",
+ "af034b33b77648b0ab4079d6444c2f1b",
+ "6ef4021dc34b4079b6306b2ade742b09",
+ "f18509bed31045bfb9848a71cb134206",
+ "845692520c5c4525a59c01e2d387b8bc",
+ "adb2383205074ae3b5e069cb6e0b61f6",
+ "0229692a2dc74c24b920dbde42b9bde4",
+ "f7201507eb1a49a88eb0ba8c2505b6f7",
+ "590c7302977245ca8449a0258fdf12be"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/5268 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "dd5e2c234b5d4cd8b5b79c9adf789799"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "epoch 0: {'precision': 0.9301581958936385, 'recall': 0.8972402597402598, 'f1': 0.9134027433482068, 'accuracy': 0.9820303761700123}\n",
+ "epoch 1: {'precision': 0.9414338606529788, 'recall': 0.9217333992420498, 'f1': 0.9314794771459496, 'accuracy': 0.9844587037146053}\n",
+ "epoch 2: {'precision': 0.9474924267923258, 'recall': 0.9241628365068942, 'f1': 0.9356822336712648, 'accuracy': 0.9862541943839407}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tqdm.auto import tqdm\n",
+ "import torch\n",
+ "\n",
+ "progress_bar = tqdm(range(num_training_steps))\n",
+ "\n",
+ "for epoch in range(num_train_epochs):\n",
+ " # Training\n",
+ " model.train()\n",
+ " for batch in train_dataloader:\n",
+ " outputs = model(**batch)\n",
+ " loss = outputs.loss\n",
+ " accelerator.backward(loss)\n",
+ "\n",
+ " optimizer.step()\n",
+ " lr_scheduler.step()\n",
+ " optimizer.zero_grad()\n",
+ " progress_bar.update(1)\n",
+ "\n",
+ " # Evaluation\n",
+ " model.eval()\n",
+ " for batch in eval_dataloader:\n",
+ " with torch.no_grad():\n",
+ " outputs = model(**batch)\n",
+ "\n",
+ " predictions = outputs.logits.argmax(dim=-1)\n",
+ " labels = batch[\"labels\"]\n",
+ "\n",
+ " # Necessary to pad predictions and labels for being gathered\n",
+ " predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n",
+ " labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
+ "\n",
+ " predictions_gathered = accelerator.gather(predictions)\n",
+ " labels_gathered = accelerator.gather(labels)\n",
+ "\n",
+ " true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n",
+ " metric.add_batch(predictions=true_predictions, references=true_labels)\n",
+ "\n",
+ " results = metric.compute()\n",
+ " print(\n",
+ " f\"epoch {epoch}:\",\n",
+ " {\n",
+ " key: results[f\"overall_{key}\"]\n",
+ " for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
+ " },\n",
+ " )\n",
+ "\n",
+ " # Save and upload\n",
+ " accelerator.wait_for_everyone()\n",
+ " unwrapped_model = accelerator.unwrap_model(model)\n",
+ " unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
+ " if accelerator.is_main_process:\n",
+ " tokenizer.save_pretrained(output_dir)\n",
+ " repo.push_to_hub(\n",
+ " commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "id": "0Ysez3KngJYO"
+ },
+ "outputs": [],
+ "source": [
+ "accelerator.wait_for_everyone()\n",
+ "unwrapped_model = accelerator.unwrap_model(model)\n",
+ "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "id": "tdGuYdgTgJYO",
+ "outputId": "9a8c194a-b4a8-47af-ff7a-e0f0344e161a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 477,
+ "referenced_widgets": [
+ "8ea52dbd98024c478512bf8a48d9f005",
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+ "[{'entity_group': 'PER',\n",
+ " 'score': 0.9988506,\n",
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+ "# Replace this with your own checkpoint\n",
+ "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
+ "token_classifier = pipeline(\n",
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