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+ } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# SpanMarker\n", + "SpanMarker is a model and library for performing span classification, originally used for Named Entity Recognition (NER). However, it could also be applied to detecting Part of Speech tokens (POS tagging). POS Tagging refers to assigning one label per word, where each label corresponds with a part of speech, such as VERB, NOUN or ADJ. Depending on the dataset, labels may be more finegrained, e.g.\n", + "```\n", + "'\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'\n", + "```\n", + "\n", + "We can use SpanMarker for this task by converting a POS Dataset into a 🤗 Dataset with `tokens` and `ner_tags` columns." + ], + "metadata": { + "id": "1Wj6hjlcdXp-" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Installing\n", + "First of all, make sure that \"GPU\" is selected as the Hardware Accelerator under `Runtime > Change runtime type`. Once set, we can install the dependencies." + ], + "metadata": { + "id": "uuG8luo0fdBH" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install span_marker -qqq" + ], + "metadata": { + "id": "C2h3OObMff7O" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Data loading and preprocessing\n", + "\n", + "Afterwards, we can load any Hugging Face Hub dataset with POS data, e.g. [batterydata/pos_tagging](https://huggingface.co/datasets/batterydata/pos_tagging). We can convert this into the format that is required for SpanMarker.\n", + "\n" + ], + "metadata": { + "id": "6-GMw_vgfjWJ" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset_dict = load_dataset(\"batterydata/pos_tagging\")\n", + "\n", + "def labels_to_ner_tags(sample):\n", + " sample[\"ner_tags\"] = [1 if label.startswith(\"V\") else 0 for label in sample.pop(\"labels\")]\n", + " sample[\"tokens\"] = sample.pop(\"words\")\n", + " return sample\n", + "\n", + "dataset_dict = dataset_dict.map(labels_to_ner_tags)\n", + "train_dataset = dataset_dict[\"train\"]\n", + "eval_dataset = dataset_dict[\"test\"]\n", + "labels = [\"O\", \"B-VERB\", \"I-VERB\"]\n", + "\n", + "train_dataset" + ], + "metadata": { + "id": "mjlsbE8NdWuI", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 138, + "referenced_widgets": [ + "58799924fd0e4fe88434d9c16e323a47", + "18a7bfd4c7bb4b579eb20897097e857d", + "ecfc044388c245678f8ee56c41465725", + "01b01f275b5d41d4ab570d22abe69e23", + "49e81c5e91a74a12b69e5458e5078481", + "f79b471e87bd4acdb62afe664b1d6257", + "70ab6f4950b14c268c2ca19f56f8ffd8", + "6f3d497f6a564b2bb54fffd1b8f7756c", + "adc9af2cc5254d2aadcd85d091222c70", + "0a590e6fa578419ebacd171dd81be980", + "0847e613e24544b68062ce6f2674a5ba", + "d0bd7ef126b449099366f503c8238bc4", + "18f61a42f4c24fd899a09b31ab661a08", + "18d1469ecaff4283934f7ecac755730f", + "0b0b56279b9047b98e57f6d14461f252", + "acf276dc3289468287dbbba358624354", + "43ecad5374934179b5552577fbb67466", + "a177d411d9e146efb6b90852b2e7d8da", + "b20764248a5a49218f6496e8df25286e", + "abc660dafe084f759accecd76b686fb9", + "baba85701cf440cd9e2a7ea09e28837b", + "e8793c097fd74ad994256fd020da347c", + "ebb44511fdca4e92b1974ba247429d14", + "2e1160fd78ea4624adca33a1fbe8df6b", + "9044585412e443b59c2bdc7f6a3652e9", + "943536c7c3ed4ae68175dc23ec8756e6", + "020107a8995346818a6e6f2f0b0b7f93", + "7342ab85823d4b81b259ccf20cb33ea3", + "cd3cbcadc2394da4864af7b589675f12", + "d6c02a768aba48c7b1ec5c0fe644e13c", + "2f2154e834fa4f459198941bb89c65b7", + "ac242d5d0c7948ceb1ab131a6581d78e", + "b456afa0f7df46c9b937842111d7b3db" + ] + }, + "outputId": "107e552e-3f10-498d-d969-7e5fa4f278ab" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/2 [00:00" + ], + "text/html": [ + "\n", + "
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+ " 'score': 0.9993988275527954,\n", + " 'char_start_index': 4,\n", + " 'char_end_index': 8},\n", + " {'span': 'try',\n", + " 'label': 'VERB',\n", + " 'score': 0.9999654293060303,\n", + " 'char_start_index': 12,\n", + " 'char_end_index': 15},\n", + " {'span': 'figure',\n", + " 'label': 'VERB',\n", + " 'score': 0.9992504715919495,\n", + " 'char_start_index': 20,\n", + " 'char_end_index': 26}]" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install spacy spacy-transformers -qqq\n", + "!spacy download en_core_web_trf" + ], + "metadata": { + "id": "XHwF7kHswoA3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import spacy_transformers\n", + "import spacy\n", + "from spacy.tokens import Doc\n", + "from tqdm.autonotebook import tqdm\n", + "import seqeval\n", + "\n", + "nlp = spacy.load(\"en_core_web_trf\")\n", + "\n", + "preds = []\n", + "golds = []\n", + "for tokens, gold_ner_tags in tqdm(zip(eval_dataset[\"tokens\"], eval_dataset[\"ner_tags\"]), total=len(eval_dataset)):\n", + " doc = nlp(Doc(nlp.vocab, tokens))\n", + " pred_ner_tags = [\"B-VERB\" if token.pos_ in (\"VERB\", \"AUX\") else \"O\" for token in doc]\n", + " gold_ner_tags = [\"B-VERB\" if tag else \"O\" for tag in gold_ner_tags]\n", + " preds.append(pred_ner_tags)\n", + " golds.append(gold_ner_tags)\n", + "\n", + "print(seqeval.metrics.classification_report(golds, preds))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192, + "referenced_widgets": [ + "55ddf1d69e7b4eed8a7eba4b26d6154e", + "a1a4b72caee44232b1025527b61905e3", + "efc200c3a80044f68a3adc5853668709", + "7db9208f041b433c8c10233a951cbd5a", + "ffd4844ae0604ff5ab4952746b9c0423", + "c922ed4949dd47539c7924ecce8bf259", + "52bb35f369b54d0f82b14a01e86e958b", + "d223c4194869496eae7446c53baf728c", + "8ad70c47ef2c4b64989d10f02839ff60", + "726da7ecc1d149fab60b834e3e917ec8", + "ffd7f7bcc7a348e285968211fd3254bd" + ] + }, + "id": "3J7GwcOVwt_c", + "outputId": "b24f5341-35a1-4189-c305-bd5f423894c7" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/1451 [00:00\n", + "\n", + "# span_marker_xlm_roberta_large_verbs\n", + "\n", + "This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.\n", + "It achieves the following results on the evaluation set:\n", + "- Loss: 0.0152\n", + "- Overall Precision: 0.9845\n", + "- Overall Recall: 0.9849\n", + "- Overall F1: 0.9847\n", + "- Overall Accuracy: 0.9962\n", + "\n", + "## Model description\n", + "\n", + "More information needed\n", + "\n", + "## Intended uses & limitations\n", + "\n", + "More information needed\n", + "\n", + "## Training and evaluation data\n", + "\n", + "More information needed\n", + "\n", + "## Training procedure\n", + "\n", + "### Training hyperparameters\n", + "\n", + "The following hyperparameters were used during training:\n", + "- learning_rate: 1e-05\n", + "- train_batch_size: 4\n", + "- eval_batch_size: 4\n", + "- seed: 42\n", + "- gradient_accumulation_steps: 2\n", + "- total_train_batch_size: 8\n", + "- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n", + "- lr_scheduler_type: linear\n", + "- lr_scheduler_warmup_ratio: 0.1\n", + "- num_epochs: 3\n", + "\n", + "### Training results\n", + "\n", + "| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |\n", + "|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|\n", + "| 0.036 | 0.61 | 1000 | 0.0151 | 0.9911 | 0.9733 | 0.9821 | 0.9956 |\n", + "| 0.0126 | 1.22 | 2000 | 0.0131 | 0.9856 | 0.9864 | 0.9860 | 0.9965 |\n", + "| 0.0175 | 1.83 | 3000 | 0.0154 | 0.9735 | 0.9894 | 0.9814 | 0.9953 |\n", + "| 0.0115 | 2.45 | 4000 | 0.0172 | 0.9821 | 0.9871 | 0.9845 | 0.9962 |\n", + "\n", + "\n", + "### Framework versions\n", + "\n", + "- Transformers 4.30.2\n", + "- Pytorch 2.0.1+cu118\n", + "- Datasets 2.13.1\n", + "- Tokenizers 0.13.3\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.push_to_hub(\"span-marker-xlm-roberta-large-verbs\")" + ], + "metadata": { + "id": "FqkHttjY0qrv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "https://huggingface.co/tomaarsen/span-marker-xlm-roberta-large-verbs" + ], + "metadata": { + "id": "4YUU-AYz8sXR" + } + } + ] +} \ No newline at end of file