{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "inE6k6hdmi7Z" }, "source": [ "training the tokenizer" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nm4TZPHtlw1J", "outputId": "91d30d5b-62c4-4af9-e596-c9a792ff41de" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/huggingface/transformers\n", " Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-jy7qmcjn\n", " Running command git clone -q https://github.com/huggingface/transformers /tmp/pip-req-build-jy7qmcjn\n", " Resolved https://github.com/huggingface/transformers to commit 30992ef0d911bdeca425969d210771118a5cd1ac\n", " Installing build dependencies ... \u001b[?25ldone\n", "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", "\u001b[?25h Preparing wheel metadata ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (21.3)\n", "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (0.12.1)\n", "Collecting regex!=2019.12.17\n", " Downloading regex-2022.8.17-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (752 kB)\n", "\u001b[K |████████████████████████████████| 752 kB 2.0 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (4.63.0)\n", "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (2.27.1)\n", "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (6.0)\n", "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (4.12.0)\n", "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (1.21.5)\n", "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (3.6.0)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.8.1 in /opt/conda/lib/python3.7/site-packages (from transformers==4.22.0.dev0) (0.8.1)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<1.0,>=0.8.1->transformers==4.22.0.dev0) (4.1.1)\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->transformers==4.22.0.dev0) (3.0.9)\n", "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers==4.22.0.dev0) (3.8.1)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.22.0.dev0) (2.0.4)\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.22.0.dev0) (3.3)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.22.0.dev0) (1.26.8)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.22.0.dev0) (2022.6.15)\n", "Building wheels for collected packages: transformers\n", " Building wheel for transformers (PEP 517) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for transformers: filename=transformers-4.22.0.dev0-py3-none-any.whl size=4730003 sha256=82f934eee756c3651864d59ed472934d9027556cf0cdf5a87b0b5b25c84c0d90\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-dmlus3m7/wheels/35/2e/a7/d819e3310040329f0f47e57c9e3e7a7338aa5e74c49acfe522\n", "Successfully built transformers\n", "Installing collected packages: regex, transformers\n", "Successfully installed regex-2022.8.17 transformers-4.22.0.dev0\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", "Requirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (1.12.0)\n", "Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.7/site-packages (from torch) (4.1.1)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", "tokenizers 0.12.1\n", "transformers 4.22.0.dev0\n" ] } ], "source": [ "!pip install git+https://github.com/huggingface/transformers \n", "!pip install torch\n", "!pip list | grep -E 'transformers|tokenizers'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "T4-fgCKVl277", "outputId": "bec7ffe2-b355-4ed1-ce22-4a0eedda0270" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "CPU times: user 1min 4s, sys: 1min 2s, total: 2min 6s\n", "Wall time: 17.4 s\n" ] } ], "source": [ "%%time \n", "from pathlib import Path\n", "from tokenizers import ByteLevelBPETokenizer\n", "\n", "paths = [str(x) for x in Path(\"./data/test\").glob(\"*.txt\")]\n", "\n", "# Initialize a tokenizer\n", "tokenizer = ByteLevelBPETokenizer()\n", "\n", "# Customize training\n", "tokenizer.train(files=paths, vocab_size=52_000, min_frequency=2, special_tokens=[\n", " \"\",\n", " \"\",\n", " \"\",\n", " \"\",\n", " \"\",\n", "])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YEgDvIj8l_pt", "outputId": "834bccd3-8c9f-46eb-8447-ed2da4b4b66f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", "To disable this warning, you can either:\n", "\t- Avoid using `tokenizers` before the fork if possible\n", "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", "mkdir: cannot create directory ‘rICE’: File exists\n" ] }, { "data": { "text/plain": [ "['rICE/vocab.json', 'rICE/merges.txt']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "!mkdir rICE\n", "tokenizer.save_model(\"rICE\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "r0NuVrIsmHCm" }, "outputs": [], "source": [ "from tokenizers.implementations import ByteLevelBPETokenizer\n", "from tokenizers.processors import BertProcessing\n", "\n", "\n", "tokenizer = ByteLevelBPETokenizer(\n", " \"./rICE/vocab.json\",\n", " \"./rICE/merges.txt\",\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vJuNibQBmWq_", "outputId": "a74394f8-e91f-432c-970b-705238c1d7b2" }, "outputs": [ { "data": { "text/plain": [ "['h', 'undurinn', 'Ġminn']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.encode(\"hundurinn minn\").tokens" ] }, { "cell_type": "markdown", "metadata": { "id": "grvjFB5BmgB1" }, "source": [ "Train the language model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0XOSmiyqn8aO", "outputId": "5d6ddada-e1b3-415d-91c9-80c6f85218cd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", "To disable this warning, you can either:\n", "\t- Avoid using `tokenizers` before the fork if possible\n", "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", "Sun Aug 21 17:31:15 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 515.65.01 Driver Version: 516.94 CUDA Version: 11.7 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 NVIDIA GeForce ... On | 00000000:01:00.0 On | N/A |\n", "| 0% 33C P8 16W / 250W | 1423MiB / 8192MiB | 18% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "# Check that we have a GPU\n", "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JzbccE9tn-yi", "outputId": "6ea45044-492a-4945-de24-a55d9518aeb1" }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check that PyTorch sees it\n", "import torch\n", "torch.cuda.is_available()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "uqo2FxM6oDgM" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Moving 0 files to the new cache system\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "0it [00:00, ?it/s]\n" ] } ], "source": [ "from transformers import RobertaConfig\n", "\n", "config = RobertaConfig(\n", " vocab_size=52_000,\n", " max_position_embeddings=514,\n", "\n", " # num_attention_heads=6,\n", " num_attention_heads=12,\n", " num_hidden_layers=6,\n", " type_vocab_size=1,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FJTTACNQoJy-", "outputId": "b074d34c-b78b-484a-c926-336483bf3439" }, "outputs": [], "source": [ "from transformers import RobertaTokenizerFast\n", "\n", "tokenizer = RobertaTokenizerFast.from_pretrained(\"./rICE\", max_len=512)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "FbkjWjH_oUhD" }, "outputs": [], "source": [ "from transformers import RobertaForMaskedLM\n", "\n", "model = RobertaForMaskedLM(config=config)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "my-YLzm0oXMr", "outputId": "2eb85d0d-5d12-4c37-a744-7c1dcd0ef1f8" }, "outputs": [ { "data": { "text/plain": [ "83504416" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.num_parameters()\n", "# => 83 million parameters" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rLOp-0VqpDGD", "outputId": "fde1de69-b551-4562-c1e2-1e93dbbf0943" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔥 data/train/41170_news.txt\n", "🔥 data/train/20077_news.txt\n", "🔥 data/train/1980_news.txt\n", "🔥 data/train/31417_news.txt\n", "🔥 data/train/16586_news.txt\n", "🔥 data/train/20125_news.txt\n", "🔥 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data/train/36332_news.txt\n", "🔥 data/train/4307_news.txt\n", "🔥 data/train/17617_news.txt\n", "🔥 data/train/44295_news.txt\n", "🔥 data/train/19131_news.txt\n", "🔥 data/train/27248_news.txt\n", "🔥 data/train/42563_news.txt\n", "🔥 data/train/17197_news.txt\n", "🔥 data/train/12527_news.txt\n", "🔥 data/train/27049_news.txt\n", "🔥 data/train/8505_news.txt\n", "🔥 data/train/42630_news.txt\n", "🔥 data/train/9852_news.txt\n", "🔥 data/train/22550_news.txt\n", "🔥 data/train/4789_news.txt\n", "🔥 data/train/38213_news.txt\n", "🔥 data/train/36810_news.txt\n", "🔥 data/train/30191_news.txt\n", "🔥 data/train/32652_news.txt\n", "🔥 data/train/25002_news.txt\n", "🔥 data/train/12861_news.txt\n", "🔥 data/train/25797_news.txt\n", "🔥 data/train/16367_news.txt\n", "🔥 data/train/10599_news.txt\n", "🔥 data/train/29950_news.txt\n", "🔥 data/train/15493_news.txt\n", "🔥 data/train/2211_news.txt\n", "🔥 data/train/11220_news.txt\n", "🔥 data/train/29072_news.txt\n" ] } ], "source": [ "from torch.utils.data import Dataset\n", "from tokenizers.implementations import ByteLevelBPETokenizer\n", "from tokenizers.processors import BertProcessing\n", "\n", "class IcelandicDataset(Dataset):\n", " def __init__(self, evaluate: bool = False):\n", " tokenizer = ByteLevelBPETokenizer(\n", " \"./rICE/vocab.json\",\n", " \"./rICE/merges.txt\",\n", " )\n", " tokenizer._tokenizer.post_processor = BertProcessing(\n", " (\"\", tokenizer.token_to_id(\"\")),\n", " (\"\", tokenizer.token_to_id(\"\")),\n", " )\n", " tokenizer.enable_truncation(max_length=128)\n", " # tokenizer.enable_truncation(max_length=512)\n", " # or use the RobertaTokenizer from `transformers` directly.\n", "\n", " self.examples = []\n", "\n", " src_files = Path(\"./data/test\").glob(\"*.txt\") if evaluate else Path(\"./data/train\").glob(\"*.txt\")\n", " for src_file in src_files:\n", " print(\"🔥\", src_file)\n", " lines = src_file.read_text(encoding=\"utf-8\").splitlines()\n", " self.examples += [x.ids for x in tokenizer.encode_batch(lines)]\n", "\n", " def __len__(self):\n", " return len(self.examples)\n", "\n", " def __getitem__(self, i):\n", " # We’ll pad at the batch level.\n", " return torch.tensor(self.examples[i])\n", "\n", "\n", "dataset = IcelandicDataset()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "29qmEMWgpjUX" }, "outputs": [], "source": [ "from transformers import DataCollatorForLanguageModeling\n", "\n", "data_collator = DataCollatorForLanguageModeling(\n", " tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BbX-A96lpkFF", "outputId": "409c6165-2ef3-4e86-bf81-a2245731bc90" }, "outputs": [], "source": [ "from transformers import Trainer, TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=\"./rICE\",\n", " overwrite_output_dir=True,\n", " num_train_epochs=1,\n", " per_device_train_batch_size=4,\n", " save_steps=10_000,\n", " save_total_limit=2,\n", " prediction_loss_only=True,\n", ")\n", "\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " data_collator=data_collator,\n", " train_dataset=dataset,\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 787 }, "id": "FXR2ulycp4oE", "outputId": "b2845f26-32aa-4e4c-a5a1-592b62109266" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " FutureWarning,\n", "***** Running training *****\n", " Num examples = 415062\n", " Num Epochs = 1\n", " Instantaneous batch size per device = 4\n", " Total train batch size (w. parallel, distributed & accumulation) = 4\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 103766\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
5008.650000
10008.208400
15008.132700
20007.974900
25007.939600
30007.915300
35007.905600
40007.802800
45007.742700
50007.714500
55007.665600
60007.689000
65007.712500
70007.588400
75007.553200
80007.556800
85007.529600
90007.509100
95007.575000
100007.404900
105007.464200
110007.469700
115007.497900
120007.375500
125007.421900
130007.326000
135007.356700
140007.316700
145007.282300
150007.310900
155007.322600
160007.278900
165007.283600
170007.275300
175007.234300
180007.235700
185007.311800
190007.233700
195007.184100
200007.187500
205007.154700
210007.131900
215007.178700
220007.069300
225007.151400
230007.122000
235007.097100
240007.078700
245007.080700
250007.081600
255007.013800
260007.062100
265006.971100
270006.989200
275006.975400
280006.983100
285006.977700
290006.924900
295006.883200
300006.882600
305006.817500
310006.909200
315006.781900
320006.829600
325006.841700
330006.765600
335006.768700
340006.753900
345006.783600
350006.695600
355006.642900
360006.702700
365006.730300
370006.603600
375006.540600
380006.599500
385006.586400
390006.561200
395006.554600
400006.466600
405006.437600
410006.476700
415006.473800
420006.413600
425006.431700
430006.421600
435006.429400
440006.340900
445006.339400
450006.357700
455006.258200
460006.280600
465006.339100
470006.357100
475006.228200
480006.306900
485006.260600
490006.231500
495006.253400
500006.181900
505006.123000
510006.191300
515006.199600
520006.175000
525006.164400
530006.096000
535006.095400
540006.069400
545006.067100
550006.072800
555006.112500
560006.075800
565006.008400
570006.060000
575006.042400
580006.122500
585006.006300
590005.935700
595005.960400
600006.014600
605005.984900
610006.004800
615005.939700
620006.000500
625005.904100
630005.935200
635005.903800
640005.947200
645005.916300
650005.908900
655005.943700
660005.868200
665005.824400
670005.851000
675005.841400
680005.867800
685005.823200
690005.824400
695005.831400
700005.857700
705005.763700
710005.830000
715005.812800
720005.751800
725005.843300
730005.762400
735005.748900
740005.754200
745005.766400
750005.724200
755005.744800
760005.659900
765005.804100
770005.712400
775005.768700
780005.756800
785005.729000
790005.736800
795005.752200
800005.745300
805005.675600
810005.764000
815005.694700
820005.665900
825005.708600
830005.640500
835005.698100
840005.732000
845005.640400
850005.679600
855005.726900
860005.680600
865005.585200
870005.675400
875005.672700
880005.724700
885005.620700
890005.629700
895005.661600
900005.656700
905005.669200
910005.595500
915005.622100
920005.619500
925005.515700
930005.516600
935005.629700
940005.639200
945005.635800
950005.606100
955005.575000
960005.605100
965005.619900
970005.635500
975005.551100
980005.590200
985005.616300
990005.592300
995005.573500
1000005.639100
1005005.596500
1010005.545400
1015005.552800
1020005.459300
1025005.486400
1030005.553900
1035005.520100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to ./rICE/checkpoint-10000\n", "Configuration saved in ./rICE/checkpoint-10000/config.json\n", "Model weights saved in ./rICE/checkpoint-10000/pytorch_model.bin\n", "Saving model checkpoint to ./rICE/checkpoint-20000\n", "Configuration saved in ./rICE/checkpoint-20000/config.json\n", "Model weights saved in ./rICE/checkpoint-20000/pytorch_model.bin\n", "Saving model checkpoint to ./rICE/checkpoint-30000\n", "Configuration saved in ./rICE/checkpoint-30000/config.json\n", "Model weights saved in ./rICE/checkpoint-30000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-10000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-40000\n", "Configuration saved in ./rICE/checkpoint-40000/config.json\n", "Model weights saved in ./rICE/checkpoint-40000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-20000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-50000\n", "Configuration saved in ./rICE/checkpoint-50000/config.json\n", "Model weights saved in ./rICE/checkpoint-50000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-30000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-60000\n", "Configuration saved in ./rICE/checkpoint-60000/config.json\n", "Model weights saved in ./rICE/checkpoint-60000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-40000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-70000\n", "Configuration saved in ./rICE/checkpoint-70000/config.json\n", "Model weights saved in ./rICE/checkpoint-70000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-50000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-80000\n", "Configuration saved in ./rICE/checkpoint-80000/config.json\n", "Model weights saved in ./rICE/checkpoint-80000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-60000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-90000\n", "Configuration saved in ./rICE/checkpoint-90000/config.json\n", "Model weights saved in ./rICE/checkpoint-90000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-70000] due to args.save_total_limit\n", "Saving model checkpoint to ./rICE/checkpoint-100000\n", "Configuration saved in ./rICE/checkpoint-100000/config.json\n", "Model weights saved in ./rICE/checkpoint-100000/pytorch_model.bin\n", "Deleting older checkpoint [rICE/checkpoint-80000] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1h 20min 36s, sys: 5min 57s, total: 1h 26min 33s\n", "Wall time: 1h 26min 15s\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=103766, training_loss=6.387869066765909, metrics={'train_runtime': 5175.7854, 'train_samples_per_second': 80.193, 'train_steps_per_second': 20.048, 'total_flos': 8553983519672448.0, 'train_loss': 6.387869066765909, 'epoch': 1.0})" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "trainer.train()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2jc5lLT91b__", "outputId": "8b3c2b62-0fb1-4cb8-b07e-4957b961d544" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to ./rICE\n", "Configuration saved in ./rICE/config.json\n", "Model weights saved in ./rICE/pytorch_model.bin\n" ] } ], "source": [ "trainer.save_model('./rICE')" ] }, { "cell_type": "markdown", "metadata": { "id": "HQuhet5f0sq8" }, "source": [ "using the model" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_hau7QBFrSB2", "outputId": "526dcd48-6faf-4105-b787-c81adcb5ed6d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file ./rICE/config.json\n", "Model config RobertaConfig {\n", " \"_name_or_path\": \"./rICE\",\n", " \"architectures\": [\n", " \"RobertaForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bos_token_id\": 0,\n", " \"classifier_dropout\": null,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 514,\n", " \"model_type\": \"roberta\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 6,\n", " \"pad_token_id\": 1,\n", " \"position_embedding_type\": \"absolute\",\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.22.0.dev0\",\n", " \"type_vocab_size\": 1,\n", " \"use_cache\": true,\n", " \"vocab_size\": 52000\n", "}\n", "\n", "loading configuration file ./rICE/config.json\n", "Model config RobertaConfig {\n", " \"_name_or_path\": \"./rICE\",\n", " \"architectures\": [\n", " \"RobertaForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bos_token_id\": 0,\n", " \"classifier_dropout\": null,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 514,\n", " \"model_type\": \"roberta\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 6,\n", " \"pad_token_id\": 1,\n", " \"position_embedding_type\": \"absolute\",\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.22.0.dev0\",\n", " \"type_vocab_size\": 1,\n", " \"use_cache\": true,\n", " \"vocab_size\": 52000\n", "}\n", "\n", "loading weights file ./rICE/pytorch_model.bin\n", "All model checkpoint weights were used when initializing RobertaForMaskedLM.\n", "\n", "All the weights of RobertaForMaskedLM were initialized from the model checkpoint at ./rICE.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForMaskedLM for predictions without further training.\n", "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", "loading configuration file ./rICE/config.json\n", "Model config RobertaConfig {\n", " \"_name_or_path\": \"./rICE\",\n", " \"architectures\": [\n", " \"RobertaForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bos_token_id\": 0,\n", " \"classifier_dropout\": null,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 514,\n", " \"model_type\": \"roberta\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 6,\n", " \"pad_token_id\": 1,\n", " \"position_embedding_type\": \"absolute\",\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.22.0.dev0\",\n", " \"type_vocab_size\": 1,\n", " \"use_cache\": true,\n", " \"vocab_size\": 52000\n", "}\n", "\n", "loading file vocab.json\n", "loading file merges.txt\n", "loading file tokenizer.json\n", "loading file added_tokens.json\n", "loading file special_tokens_map.json\n", "loading file tokenizer_config.json\n", "loading configuration file ./rICE/config.json\n", "Model config RobertaConfig {\n", " \"_name_or_path\": \"./rICE\",\n", " \"architectures\": [\n", " \"RobertaForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bos_token_id\": 0,\n", " \"classifier_dropout\": null,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 514,\n", " \"model_type\": \"roberta\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 6,\n", " \"pad_token_id\": 1,\n", " \"position_embedding_type\": \"absolute\",\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.22.0.dev0\",\n", " \"type_vocab_size\": 1,\n", " \"use_cache\": true,\n", " \"vocab_size\": 52000\n", "}\n", "\n", "loading configuration file ./rICE/config.json\n", "Model config RobertaConfig {\n", " \"_name_or_path\": \"./rICE\",\n", " \"architectures\": [\n", " \"RobertaForMaskedLM\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"bos_token_id\": 0,\n", " \"classifier_dropout\": null,\n", " \"eos_token_id\": 2,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 514,\n", " \"model_type\": \"roberta\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 6,\n", " \"pad_token_id\": 1,\n", " \"position_embedding_type\": \"absolute\",\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.22.0.dev0\",\n", " \"type_vocab_size\": 1,\n", " \"use_cache\": true,\n", " \"vocab_size\": 52000\n", "}\n", "\n" ] } ], "source": [ "from transformers import pipeline\n", "\n", "fill_mask = pipeline(\n", " \"fill-mask\",\n", " model=\"./rICE\",\n", " tokenizer=\"./rICE\"\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GS8IdoR40uyX", "outputId": "af7934cb-b243-4f40-ac3b-8fc370dc1cd5" }, "outputs": [ { "data": { "text/plain": [ "[{'score': 0.10871271789073944,\n", " 'token': 684,\n", " 'token_str': ' eins',\n", " 'sequence': \"Ég heiti Aron og elska að borða eins og epli'\"},\n", " {'score': 0.01858321763575077,\n", " 'token': 650,\n", " 'token_str': ' vel',\n", " 'sequence': \"Ég heiti Aron og elska að borða vel og epli'\"},\n", " {'score': 0.010185176506638527,\n", " 'token': 322,\n", " 'token_str': ' við',\n", " 'sequence': \"Ég heiti Aron og elska að borða við og epli'\"},\n", " {'score': 0.007654798217117786,\n", " 'token': 980,\n", " 'token_str': ' áfram',\n", " 'sequence': \"Ég heiti Aron og elska að borða áfram og epli'\"},\n", " {'score': 0.007473105099052191,\n", " 'token': 1289,\n", " 'token_str': ' heima',\n", " 'sequence': \"Ég heiti Aron og elska að borða heima og epli'\"}]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fill_mask(\"Ég heiti Aron og elska að borða og epli'\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U8YMbEoR9AVV", "outputId": "e82df3b2-a99d-4457-8ec0-01317a9c93ca" }, "outputs": [], "source": [ "!zip -r /content/file.zip /content/rICE/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "vgpzkzMI9B5v", "outputId": "76a77a6a-361d-465a-e1c5-ac8dafbc602e" }, "outputs": [], "source": [ "# from google.colab import files\n", "# files.download(\"/content/file.zip\")" ] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "mICE.ipynb", "provenance": [] }, "gpuClass": "standard", "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": "3.7.13" }, "vscode": { "interpreter": { "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" } } }, "nbformat": 4, "nbformat_minor": 4 }