{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "sMP4HRhzwPyj", "metadata": { "id": "sMP4HRhzwPyj" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "id": "3564591f-25e4-4be6-9104-acec74b5a120", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3564591f-25e4-4be6-9104-acec74b5a120", "outputId": "262ba65f-7c8f-4297-fc3c-cbbec9a45dbe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: datasets in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (2.16.1)\n", "Requirement already satisfied: tqdm>=4.62.1 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from datasets) (4.66.1)\n", "Requirement already satisfied: multiprocess in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from datasets) (0.70.15)\n", "Requirement already satisfied: huggingface-hub>=0.19.4 in 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"name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 23.0.1 -> 23.3.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "!pip install datasets" ] }, { "cell_type": "code", "execution_count": 2, "id": "bd4b0343-d24f-4ca0-9fcb-94307eaaf732", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bd4b0343-d24f-4ca0-9fcb-94307eaaf732", "outputId": "73f83898-df93-4271-993f-41b91be22eac" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: ipywidgets in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (8.1.1)\n", "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from ipywidgets) (3.0.9)\n", "Requirement already satisfied: traitlets>=4.3.1 in 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"outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_17672\\3078022459.py:1: FutureWarning: list_datasets is deprecated and will be removed in the next major version of datasets. Use 'huggingface_hub.list_datasets' instead.\n", " all_ds = datasets.list_datasets()\n" ] }, { "data": { "text/plain": [ "100953" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_ds = datasets.list_datasets()\n", "len(all_ds)" ] }, { "cell_type": "code", "execution_count": 6, "id": "f20dcb4d-bd17-4bf4-839f-3013b4f40c84", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f20dcb4d-bd17-4bf4-839f-3013b4f40c84", "outputId": "1161acd5-97b8-4b0a-c93f-683b1838c597" }, "outputs": [ { "data": { "text/plain": [ "['acronym_identification',\n", " 'ade_corpus_v2',\n", " 'adversarial_qa',\n", " 'aeslc',\n", " 'afrikaans_ner_corpus']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_ds[:5]" ] }, { "cell_type": "code", "execution_count": 7, "id": "63e676b3-0ce3-462a-b9e3-cc37c2f33703", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 169, "referenced_widgets": [ "05cc2ce67be34a029089aa9064392ca6", "e99d62aef49a4a7d85f86ebbac5ab786", "ca827a559f9e4d0d99b408981a645287", "7128e8b45e604f44af746f27b2e351bc", "da0bf0f0a9454be8ae9a8a95ca37315d", "c7ebc401b9584e5dae027ae7dd7141f5", "ecace9056b82426190cb2d3c2b4ccd28", "5a88f2286c7e4704b7b4defad53b2a27", "e81220f2f5ef44a2bbf1fabd4dbfa8e8", "c6aa521ffcad4eeb81326c53e363dd2e", "e0b97ccf15be4026b764f9925f631c33", "0c3d289d974f491d9f2edd93a17cd4f3", "3d9010a4bfcf4d66b3652fc7aca5513e", "1c36368b20d4410b92e3c2be7b8823aa", "dc68447c05424480b23caab1c55d4acf", "596e84f852484fd88f045c6451a39005", "d49eb7b8d7bc4b51918c755062175476", "69f952a2aa274616a5d92134e1da69d3", "e86a2c0d7e614372a745df9262260e01", "d3c05033a4484d129a5cdbc2d05fb925", "09fd29c3ab924c6f879c490f57fdc515", "55f94c32986047d9aceaceb933875539", "ec8a4b6011ae46769c4531b46ff15875", "b578afec38ba4964b59379a074c3ff50", "1bd119557428468eac53ebb939dd1dba", "9fe88a4505474eb6ae6d3932ee462101", "7787e51fabb54ebc93c6e2e6fcc93463", "8b55171076fb461384fe88c5d721f0ad", "a8db1128ee054cc0b441db3a192f1be3", "0ec06e317e7d4aa1b33b49a820409a36", "dd80935d4c224cfd828950b714c82aa2", "91ff43250f204bdfaa6668094cbb93d2", "6c00fea3446c4f54b1c68b1b662f5f56" ] }, "id": "63e676b3-0ce3-462a-b9e3-cc37c2f33703", "outputId": "fe395356-79d1-4476-bfd9-411f9dcbde23" }, "outputs": [], "source": [ "dataset = datasets.load_dataset('NLPC-UOM/Sinhala-News-Source-classification')" ] }, { "cell_type": "code", "execution_count": 8, "id": "b6593b70-5caa-4da2-92bb-594e67bd964c", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "b6593b70-5caa-4da2-92bb-594e67bd964c", "outputId": "beb37a8b-af23-42b9-c2d8-511b8d097752" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['Unnamed: 0', 'comment', 'label'],\n", " num_rows: 24094\n", " })\n", "})" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset" ] }, { "cell_type": "code", "execution_count": 9, "id": "d80f383a-3928-48cb-9218-50b2e6a5577b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d80f383a-3928-48cb-9218-50b2e6a5577b", "outputId": "61ba094e-5024-42b1-924c-7350680f1e6e" }, "outputs": [ { "data": { "text/plain": [ "{'Unnamed: 0': [0, 1, 2, 3, 4],\n", " 'comment': ['ත්\\u200dරිකුණාමලයේ දී නැගෙනහිර ආරක්ෂක සේනා මූලස්ථානය අලුත් අවුරුදු සමරයි',\n", " '59 වන සේනාංකය අලුත් අවුරුද්ද සමරයි',\n", " 'නිවාඩු නොමැතිව සේවයට නොපැමිණ සිටින ත්\\u200dරිවිධ හමුදා සාමාජිකයින් සඳහා මසක පොදු සමා කාලයක් ප්\\u200dරකාශයට පත් කෙරේ',\n", " 'මුලතිව් ආරක්ෂක සේනා මුලස්ථානයේ බදාදා දිනයන් හිදී අධ්\\u200dයාපනික දේශන වැඩසටහන්',\n", " 'බෝයගනේ විජයබාහු පාබල රෙජිමේන්තු මධ්\\u200dයස්ථානයට සපැමිණි යුද්ධ හමුදාධිපතිතුමන් මහත් හරසරින් පිළිගැනේ'],\n", " 'label': [0, 0, 0, 0, 0]}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['train'][:5]" ] }, { "cell_type": "code", "execution_count": 25, "id": "b6c82283-ce4c-491e-81c1-ba0e512f0704", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "346960a10e0f40af9e8352aaa35c4be9", "0f461807d1cd4835aa4abdb849c4f598", "b36a9743b1c64bbaa4f3acd294c30db0", "711c7cc532aa4643bb76097e5a4da939", "590abed7bad6459ebd2e7b8a2578ddca", "c6e5bcfc2c64440a8c13575b663647d9", "d3f2b3d9b13e4572932b2d339ee52e84", "0f844872396a4c0b8e5a0b1afa0ee51a", "96cae566bff84a8e805958fa53592583", "cbdc623872e54103bfda15e04eae870b", "116822940ef34c7f9aea28c322d7679c" ] }, "id": "b6c82283-ce4c-491e-81c1-ba0e512f0704", "outputId": "94b358c7-6f04-4af5-f7e6-0398f35469f5" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "45099238965c446a83710fe4aee69d12", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/24094 [00:00=0.16.4 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from tokenizers) (0.20.3)\n", "Requirement already satisfied: 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urllib3<3,>=1.21.1 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->huggingface_hub<1.0,>=0.16.4->tokenizers) (2.1.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->huggingface_hub<1.0,>=0.16.4->tokenizers) (2023.11.17)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 23.0.1 -> 23.3.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "!pip install tokenizers" ] }, { "cell_type": "code", "execution_count": 14, "id": "225c9c9a-0182-4bfe-92fc-2234e3515560", "metadata": { "id": "225c9c9a-0182-4bfe-92fc-2234e3515560" }, "outputs": [], "source": [ "from tokenizers import ByteLevelBPETokenizer" ] }, { "cell_type": "code", "execution_count": 15, "id": "501098a0-5df1-448b-99e6-52143cb6751f", "metadata": { "id": "501098a0-5df1-448b-99e6-52143cb6751f" }, "outputs": [], "source": [ "tokenizer = ByteLevelBPETokenizer()" ] }, { "cell_type": "code", "execution_count": 16, "id": "24ef02fe-5703-4b87-a92a-e6e936f7fd96", "metadata": { "id": "24ef02fe-5703-4b87-a92a-e6e936f7fd96" }, "outputs": [], "source": [ "tokenizer.train(files=paths, vocab_size=30_522, min_frequency=2,\n", " special_tokens=['', '', '', ''\n", " ])" ] }, { "cell_type": "code", "execution_count": 17, "id": "da5bfba4-6c97-4256-b669-f75026b93e09", "metadata": { "id": "da5bfba4-6c97-4256-b669-f75026b93e09" }, "outputs": [], "source": [ "import os\n", "os.mkdir('sinhalaMLM5')" ] }, { "cell_type": "code", "execution_count": 18, "id": "e25beeaa-e969-4c33-98e2-65523d827d95", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e25beeaa-e969-4c33-98e2-65523d827d95", "outputId": "28a1ea45-4c25-4bb2-f626-1a388bb23697" }, "outputs": [ { "data": { "text/plain": [ "['sinhalaMLM5\\\\vocab.json', 'sinhalaMLM5\\\\merges.txt']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.save_model('sinhalaMLM5')" ] }, { "cell_type": "code", "execution_count": 19, "id": "222a9edf-15ed-44a8-aaba-2afee76b3cbf", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "222a9edf-15ed-44a8-aaba-2afee76b3cbf", "outputId": "d3f76f56-ec03-45ab-c75a-f5062fbf0e0d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: transformers in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (4.37.1)\n", "Requirement already satisfied: numpy>=1.17 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (1.26.3)\n", "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (6.0.1)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (23.2)\n", "Requirement already satisfied: tokenizers<0.19,>=0.14 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (0.15.1)\n", "Requirement already satisfied: requests in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (2.31.0)\n", "Requirement already satisfied: tqdm>=4.27 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (4.66.1)\n", "Requirement already satisfied: filelock in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (3.13.1)\n", "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (2023.12.25)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (0.20.3)\n", "Requirement already satisfied: safetensors>=0.3.1 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from transformers) (0.4.2)\n", "Requirement already satisfied: fsspec>=2023.5.0 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.10.0)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.9.0)\n", "Requirement already satisfied: colorama in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from tqdm>=4.27->transformers) (0.4.6)\n", "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->transformers) (2023.11.17)\n", "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->transformers) (3.6)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->transformers) (3.3.2)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\dell\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests->transformers) (2.1.0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "[notice] A new release of pip is available: 23.0.1 -> 23.3.2\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], "source": [ "!pip install transformers" ] }, { "cell_type": "code", "execution_count": null, "id": "864266b5-77d5-451e-9c01-096588ff62e4", "metadata": { "id": "864266b5-77d5-451e-9c01-096588ff62e4" }, "outputs": [], "source": [ "#pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cpu" ] }, { "cell_type": "code", "execution_count": 20, "id": "67d2fc2e-9cb0-4306-9769-0c34a2111c37", "metadata": { "id": "67d2fc2e-9cb0-4306-9769-0c34a2111c37" }, "outputs": [], "source": [ "from transformers import RobertaTokenizerFast" ] }, { "cell_type": "code", "execution_count": 21, "id": "474f1e8c-e499-4205-96cc-44f4a4c9e4e3", "metadata": { "id": "474f1e8c-e499-4205-96cc-44f4a4c9e4e3" }, "outputs": [], "source": [ "tokenizer = RobertaTokenizerFast.from_pretrained('sinhalaMLM5')" ] }, { "cell_type": "code", "execution_count": 22, "id": "3ea07602-9cd6-4c12-a860-9302e4db7607", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3ea07602-9cd6-4c12-a860-9302e4db7607", "outputId": "779097d6-5ec2-4359-fbf1-3decd11d0a36" }, "outputs": [ { "data": { "text/plain": [ "{'input_ids': [0, 3365, 266, 305, 282, 558, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1]}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer('ළමයා ගෙදර')" ] }, { "cell_type": "code", "execution_count": 23, "id": "90cec429-ee77-4c34-814b-3fec3f5b035e", "metadata": { "id": "90cec429-ee77-4c34-814b-3fec3f5b035e" }, "outputs": [], "source": [ "import torch\n", "def mlm(tensor):\n", " rand = torch.rand(tensor.shape)\n", " mask_arr = (rand < 0.15) * (tensor > 2)\n", " for i in range( tensor.shape[0]):\n", " selection = torch.flatten(mask_arr[i].nonzero()).tolist() #[[2,5,8]]\n", " tensor[i, selection] = 3\n", " return tensor" ] }, { "cell_type": "code", "execution_count": 24, "id": "2c22dc9d-cf4f-4cf0-a7b4-364464c525d7", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2c22dc9d-cf4f-4cf0-a7b4-364464c525d7", "outputId": "6b42dd3f-a7ed-4821-a446-9618127c7038" }, "outputs": [ { "data": { "text/plain": [ "['sinhala_0.txt', 'sinhala_1.txt', 'sinhala_2.txt']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "\n", "paths = [str(x) for x in Path('./').glob('*.txt')]\n", "paths[:3]" ] }, { "cell_type": "code", "execution_count": 25, "id": "d19907e4-e54d-4596-a8a8-099e6965bcef", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "deaa44b4f85f41ac8bf9785a290d7496", "c636f70868d644f283af9494fe3ef5c2", "c795d84cf0e04e9dbe31c66fee2da9ba", "fac79cdf5b72425ca2770fa0dcf32be0", "3455cfbc927a44628d2c44ad251faf88", "7f7f942183f042b58895d4f6f5206238", "eb9541ecc16c488a9d770b3fdb7e08f0", "df8d8240b9af4cbba91d88c3e886fb55", "6aa7e04512f244d1aebdba9a6bf4cbd0", "7b66539e5b504cc3ba9bc91c36c43002", "4cec3f0b3c09470697dcc3d643527379" ] }, "id": "d19907e4-e54d-4596-a8a8-099e6965bcef", "outputId": "c3085b91-bce2-4d5a-b3d5-ec101799ded5" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0d69317043be4bdb9adcf9017673a970", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00 1\u001b[0m fill \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfill-mask\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msinhalaMLM6\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokenizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msinhalaMLM6\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\pipelines\\__init__.py:967\u001b[0m, in \u001b[0;36mpipeline\u001b[1;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[0;32m 964\u001b[0m tokenizer_kwargs \u001b[38;5;241m=\u001b[39m model_kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 965\u001b[0m tokenizer_kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_dtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 967\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[0;32m 968\u001b[0m tokenizer_identifier, use_fast\u001b[38;5;241m=\u001b[39muse_fast, _from_pipeline\u001b[38;5;241m=\u001b[39mtask, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhub_kwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtokenizer_kwargs\n\u001b[0;32m 969\u001b[0m )\n\u001b[0;32m 971\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m load_image_processor:\n\u001b[0;32m 972\u001b[0m \u001b[38;5;66;03m# Try to infer image processor from model or config name (if provided as str)\u001b[39;00m\n\u001b[0;32m 973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image_processor \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py:832\u001b[0m, in \u001b[0;36mAutoTokenizer.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m 830\u001b[0m tokenizer_class_py, tokenizer_class_fast \u001b[38;5;241m=\u001b[39m TOKENIZER_MAPPING[\u001b[38;5;28mtype\u001b[39m(config)]\n\u001b[0;32m 831\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tokenizer_class_fast \u001b[38;5;129;01mand\u001b[39;00m (use_fast \u001b[38;5;129;01mor\u001b[39;00m tokenizer_class_py \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 832\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tokenizer_class_fast\u001b[38;5;241m.\u001b[39mfrom_pretrained(pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39minputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 833\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 834\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tokenizer_class_py \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\tokenization_utils_base.py:2013\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, *init_inputs, **kwargs)\u001b[0m\n\u001b[0;32m 2007\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\n\u001b[0;32m 2008\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt load following files from cache: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00munresolved_files\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and cannot check if these \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2009\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles are necessary for the tokenizer to operate.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2010\u001b[0m )\n\u001b[0;32m 2012\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(full_file_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m full_file_name \u001b[38;5;129;01min\u001b[39;00m resolved_vocab_files\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m-> 2013\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[0;32m 2014\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt load tokenizer for \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpretrained_model_name_or_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. If you were trying to load it from \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2015\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://huggingface.co/models\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, make sure you don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt have a local directory with the same name. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2016\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOtherwise, make sure \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpretrained_model_name_or_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is the correct path to a directory \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2017\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontaining all relevant files for a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m tokenizer.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2018\u001b[0m )\n\u001b[0;32m 2020\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m file_id, file_path \u001b[38;5;129;01min\u001b[39;00m vocab_files\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 2021\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file_id \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m resolved_vocab_files:\n", "\u001b[1;31mOSError\u001b[0m: Can't load tokenizer for 'sinhalaMLM6'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'sinhalaMLM6' is the correct path to a directory containing all relevant files for a RobertaTokenizerFast tokenizer." ] } ], "source": [ "fill = pipeline('fill-mask', model='sinhalaMLM6', tokenizer='sinhalaMLM6')" ] }, { "cell_type": "code", "execution_count": null, "id": "zWf4QTD7Hn5u", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "zWf4QTD7Hn5u", "outputId": "ddac19e4-31c6-43f0-c4f0-eeae66603fe7" }, "outputs": [], "source": [ "fill(f'මම ගෙදර ය{fill.tokenizer.mask_token}')" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { 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