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+ },
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+ "cells": [
514
+ {
515
+ "cell_type": "markdown",
516
+ "metadata": {
517
+ "id": "view-in-github",
518
+ "colab_type": "text"
519
+ },
520
+ "source": [
521
+ "<a href=\"https://colab.research.google.com/github/huggingface/blog/blob/notebook_update_may15/notebooks/01_how_to_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
522
+ ]
523
+ },
524
+ {
525
+ "cell_type": "code",
526
+ "metadata": {
527
+ "id": "e67Ut53QYEdU",
528
+ "colab_type": "code",
529
+ "cellView": "form",
530
+ "outputId": "437871b8-b8ac-4eaf-c2e1-61d801c5e6b2",
531
+ "colab": {
532
+ "base_uri": "https://localhost:8080/",
533
+ "height": 100
534
+ }
535
+ },
536
+ "source": [
537
+ "#@title\n",
538
+ "%%html\n",
539
+ "<div style=\"background-color: pink;\">\n",
540
+ " Notebook written in collaboration with <a href=\"https://github.com/aditya-malte\">Aditya Malte</a>.\n",
541
+ " <br>\n",
542
+ " The Notebook is on GitHub, so contributions are more than welcome.\n",
543
+ "</div>\n",
544
+ "<br>\n",
545
+ "<div style=\"background-color: yellow;\">\n",
546
+ " Aditya wrote another notebook with a slightly different use case and methodology, please check it out.\n",
547
+ " <br>\n",
548
+ " <a target=\"_blank\" href=\"https://gist.github.com/aditya-malte/2d4f896f471be9c38eb4d723a710768b\">\n",
549
+ " https://gist.github.com/aditya-malte/2d4f896f471be9c38eb4d723a710768b\n",
550
+ " </a>\n",
551
+ "</div>\n"
552
+ ],
553
+ "execution_count": 0,
554
+ "outputs": [
555
+ {
556
+ "output_type": "display_data",
557
+ "data": {
558
+ "text/html": [
559
+ "<div style=\"background-color: pink;\">\n",
560
+ " Notebook written in collaboration with <a href=\"https://github.com/aditya-malte\">Aditya Malte</a>.\n",
561
+ " <br>\n",
562
+ " The Notebook is on GitHub, so contributions are more than welcome.\n",
563
+ "</div>\n",
564
+ "<br>\n",
565
+ "<div style=\"background-color: yellow;\">\n",
566
+ " Aditya wrote another notebook with a slightly different use case and methodology, please check it out.\n",
567
+ " <br>\n",
568
+ " <a target=\"_blank\" href=\"https://gist.github.com/aditya-malte/2d4f896f471be9c38eb4d723a710768b\">\n",
569
+ " https://gist.github.com/aditya-malte/2d4f896f471be9c38eb4d723a710768b\n",
570
+ " </a>\n",
571
+ "</div>"
572
+ ],
573
+ "text/plain": [
574
+ "<IPython.core.display.HTML object>"
575
+ ]
576
+ },
577
+ "metadata": {
578
+ "tags": []
579
+ }
580
+ }
581
+ ]
582
+ },
583
+ {
584
+ "cell_type": "markdown",
585
+ "metadata": {
586
+ "id": "M1oqh0F6W3ad",
587
+ "colab_type": "text"
588
+ },
589
+ "source": [
590
+ "# How to train a new language model from scratch using Transformers and Tokenizers\n",
591
+ "\n",
592
+ "### Notebook edition (link to blogpost [link](https://huggingface.co/blog/how-to-train)). Last update May 15, 2020\n",
593
+ "\n",
594
+ "\n",
595
+ "Over the past few months, we made several improvements to our [`transformers`](https://github.com/huggingface/transformers) and [`tokenizers`](https://github.com/huggingface/tokenizers) libraries, with the goal of making it easier than ever to **train a new language model from scratch**.\n",
596
+ "\n",
597
+ "In this post we’ll demo how to train a “small” model (84 M parameters = 6 layers, 768 hidden size, 12 attention heads) – that’s the same number of layers & heads as DistilBERT – on **Esperanto**. We’ll then fine-tune the model on a downstream task of part-of-speech tagging.\n"
598
+ ]
599
+ },
600
+ {
601
+ "cell_type": "markdown",
602
+ "metadata": {
603
+ "id": "oK7PPVm2XBgr",
604
+ "colab_type": "text"
605
+ },
606
+ "source": [
607
+ "## 1. Find a dataset\n",
608
+ "\n",
609
+ "First, let us find a corpus of text in Esperanto. Here we’ll use the Esperanto portion of the [OSCAR corpus](https://traces1.inria.fr/oscar/) from INRIA.\n",
610
+ "OSCAR is a huge multilingual corpus obtained by language classification and filtering of [Common Crawl](https://commoncrawl.org/) dumps of the Web.\n",
611
+ "\n",
612
+ "<img src=\"https://huggingface.co/blog/assets/01_how-to-train/oscar.png\" style=\"margin: auto; display: block; width: 260px;\">\n",
613
+ "\n",
614
+ "The Esperanto portion of the dataset is only 299M, so we’ll concatenate with the Esperanto sub-corpus of the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download), which is comprised of text from diverse sources like news, literature, and wikipedia.\n",
615
+ "\n",
616
+ "The final training corpus has a size of 3 GB, which is still small – for your model, you will get better results the more data you can get to pretrain on. \n",
617
+ "\n"
618
+ ]
619
+ },
620
+ {
621
+ "cell_type": "code",
622
+ "metadata": {
623
+ "id": "HOk4iZ9YZvec",
624
+ "colab_type": "code",
625
+ "colab": {}
626
+ },
627
+ "source": [
628
+ "# in this notebook we'll only get one of the files (the Oscar one) for the sake of simplicity and performance\n",
629
+ "!wget -c https://cdn-datasets.huggingface.co/EsperBERTo/data/oscar.eo.txt"
630
+ ],
631
+ "execution_count": 0,
632
+ "outputs": []
633
+ },
634
+ {
635
+ "cell_type": "markdown",
636
+ "metadata": {
637
+ "id": "G-kkz81OY6xH",
638
+ "colab_type": "text"
639
+ },
640
+ "source": [
641
+ "## 2. Train a tokenizer\n",
642
+ "\n",
643
+ "We choose to train a byte-level Byte-pair encoding tokenizer (the same as GPT-2), with the same special tokens as RoBERTa. Let’s arbitrarily pick its size to be 52,000.\n",
644
+ "\n",
645
+ "We recommend training a byte-level BPE (rather than let’s say, a WordPiece tokenizer like BERT) because it will start building its vocabulary from an alphabet of single bytes, so all words will be decomposable into tokens (no more `<unk>` tokens!).\n"
646
+ ]
647
+ },
648
+ {
649
+ "cell_type": "code",
650
+ "metadata": {
651
+ "id": "5duRggBRZKvP",
652
+ "colab_type": "code",
653
+ "colab": {}
654
+ },
655
+ "source": [
656
+ "# We won't need TensorFlow here\n",
657
+ "!pip uninstall -y tensorflow\n",
658
+ "# Install `transformers` from master\n",
659
+ "!pip install git+https://github.com/huggingface/transformers\n",
660
+ "!pip list | grep -E 'transformers|tokenizers'\n",
661
+ "# transformers version at notebook update --- 2.11.0\n",
662
+ "# tokenizers version at notebook update --- 0.8.0rc1"
663
+ ],
664
+ "execution_count": 0,
665
+ "outputs": []
666
+ },
667
+ {
668
+ "cell_type": "code",
669
+ "metadata": {
670
+ "id": "IMnymRDLe0hi",
671
+ "colab_type": "code",
672
+ "outputId": "4d26476f-e6b5-475a-a0c1-41b6fcdc041a",
673
+ "colab": {
674
+ "base_uri": "https://localhost:8080/",
675
+ "height": 52
676
+ }
677
+ },
678
+ "source": [
679
+ "%%time \n",
680
+ "from pathlib import Path\n",
681
+ "\n",
682
+ "from tokenizers import ByteLevelBPETokenizer\n",
683
+ "\n",
684
+ "paths = [str(x) for x in Path(\".\").glob(\"**/*.txt\")]\n",
685
+ "\n",
686
+ "# Initialize a tokenizer\n",
687
+ "tokenizer = ByteLevelBPETokenizer()\n",
688
+ "\n",
689
+ "# Customize training\n",
690
+ "tokenizer.train(files=paths, vocab_size=52_000, min_frequency=2, special_tokens=[\n",
691
+ " \"<s>\",\n",
692
+ " \"<pad>\",\n",
693
+ " \"</s>\",\n",
694
+ " \"<unk>\",\n",
695
+ " \"<mask>\",\n",
696
+ "])"
697
+ ],
698
+ "execution_count": 3,
699
+ "outputs": [
700
+ {
701
+ "output_type": "stream",
702
+ "text": [
703
+ "CPU times: user 4min, sys: 3min 7s, total: 7min 7s\n",
704
+ "Wall time: 2min 25s\n"
705
+ ],
706
+ "name": "stdout"
707
+ }
708
+ ]
709
+ },
710
+ {
711
+ "cell_type": "markdown",
712
+ "metadata": {
713
+ "id": "6Ei7bqpRf1LH",
714
+ "colab_type": "text"
715
+ },
716
+ "source": [
717
+ "Now let's save files to disk"
718
+ ]
719
+ },
720
+ {
721
+ "cell_type": "code",
722
+ "metadata": {
723
+ "id": "EIS-irI0f32P",
724
+ "colab_type": "code",
725
+ "outputId": "e86c4a24-eb65-4f0a-aa58-ed1931a05ac9",
726
+ "colab": {
727
+ "base_uri": "https://localhost:8080/",
728
+ "height": 34
729
+ }
730
+ },
731
+ "source": [
732
+ "!mkdir EsperBERTo\n",
733
+ "tokenizer.save_model(\"EsperBERTo\")"
734
+ ],
735
+ "execution_count": 4,
736
+ "outputs": [
737
+ {
738
+ "output_type": "execute_result",
739
+ "data": {
740
+ "text/plain": [
741
+ "['EsperBERTo/vocab.json', 'EsperBERTo/merges.txt']"
742
+ ]
743
+ },
744
+ "metadata": {
745
+ "tags": []
746
+ },
747
+ "execution_count": 4
748
+ }
749
+ ]
750
+ },
751
+ {
752
+ "cell_type": "markdown",
753
+ "metadata": {
754
+ "id": "lOOfYSuQhSqT",
755
+ "colab_type": "text"
756
+ },
757
+ "source": [
758
+ "🔥🔥 Wow, that was fast! ⚡️🔥\n",
759
+ "\n",
760
+ "We now have both a `vocab.json`, which is a list of the most frequent tokens ranked by frequency, and a `merges.txt` list of merges.\n",
761
+ "\n",
762
+ "```json\n",
763
+ "{\n",
764
+ "\t\"<s>\": 0,\n",
765
+ "\t\"<pad>\": 1,\n",
766
+ "\t\"</s>\": 2,\n",
767
+ "\t\"<unk>\": 3,\n",
768
+ "\t\"<mask>\": 4,\n",
769
+ "\t\"!\": 5,\n",
770
+ "\t\"\\\"\": 6,\n",
771
+ "\t\"#\": 7,\n",
772
+ "\t\"$\": 8,\n",
773
+ "\t\"%\": 9,\n",
774
+ "\t\"&\": 10,\n",
775
+ "\t\"'\": 11,\n",
776
+ "\t\"(\": 12,\n",
777
+ "\t\")\": 13,\n",
778
+ "\t# ...\n",
779
+ "}\n",
780
+ "\n",
781
+ "# merges.txt\n",
782
+ "l a\n",
783
+ "Ġ k\n",
784
+ "o n\n",
785
+ "Ġ la\n",
786
+ "t a\n",
787
+ "Ġ e\n",
788
+ "Ġ d\n",
789
+ "Ġ p\n",
790
+ "# ...\n",
791
+ "```\n",
792
+ "\n",
793
+ "What is great is that our tokenizer is optimized for Esperanto. Compared to a generic tokenizer trained for English, more native words are represented by a single, unsplit token. Diacritics, i.e. accented characters used in Esperanto – `ĉ`, `ĝ`, `ĥ`, `ĵ`, `ŝ`, and `ŭ` – are encoded natively. We also represent sequences in a more efficient manner. Here on this corpus, the average length of encoded sequences is ~30% smaller as when using the pretrained GPT-2 tokenizer.\n",
794
+ "\n",
795
+ "Here’s how you can use it in `tokenizers`, including handling the RoBERTa special tokens – of course, you’ll also be able to use it directly from `transformers`.\n"
796
+ ]
797
+ },
798
+ {
799
+ "cell_type": "code",
800
+ "metadata": {
801
+ "id": "tKVWB8WShT-z",
802
+ "colab_type": "code",
803
+ "colab": {}
804
+ },
805
+ "source": [
806
+ "from tokenizers.implementations import ByteLevelBPETokenizer\n",
807
+ "from tokenizers.processors import BertProcessing\n",
808
+ "\n",
809
+ "\n",
810
+ "tokenizer = ByteLevelBPETokenizer(\n",
811
+ " \"./EsperBERTo/vocab.json\",\n",
812
+ " \"./EsperBERTo/merges.txt\",\n",
813
+ ")"
814
+ ],
815
+ "execution_count": 0,
816
+ "outputs": []
817
+ },
818
+ {
819
+ "cell_type": "code",
820
+ "metadata": {
821
+ "id": "hO5M3vrAhcuj",
822
+ "colab_type": "code",
823
+ "colab": {}
824
+ },
825
+ "source": [
826
+ "tokenizer._tokenizer.post_processor = BertProcessing(\n",
827
+ " (\"</s>\", tokenizer.token_to_id(\"</s>\")),\n",
828
+ " (\"<s>\", tokenizer.token_to_id(\"<s>\")),\n",
829
+ ")\n",
830
+ "tokenizer.enable_truncation(max_length=512)"
831
+ ],
832
+ "execution_count": 0,
833
+ "outputs": []
834
+ },
835
+ {
836
+ "cell_type": "code",
837
+ "metadata": {
838
+ "id": "E3Ye27nchfzq",
839
+ "colab_type": "code",
840
+ "outputId": "b9812ed2-1ecd-4e1b-d9bd-7de581955e70",
841
+ "colab": {
842
+ "base_uri": "https://localhost:8080/",
843
+ "height": 34
844
+ }
845
+ },
846
+ "source": [
847
+ "tokenizer.encode(\"Mi estas Julien.\")"
848
+ ],
849
+ "execution_count": 0,
850
+ "outputs": [
851
+ {
852
+ "output_type": "execute_result",
853
+ "data": {
854
+ "text/plain": [
855
+ "Encoding(num_tokens=7, attributes=[ids, type_ids, tokens, offsets, attention_mask, special_tokens_mask, overflowing])"
856
+ ]
857
+ },
858
+ "metadata": {
859
+ "tags": []
860
+ },
861
+ "execution_count": 10
862
+ }
863
+ ]
864
+ },
865
+ {
866
+ "cell_type": "code",
867
+ "metadata": {
868
+ "id": "X8ya5_7rhjKS",
869
+ "colab_type": "code",
870
+ "outputId": "e9e08ded-1081-4823-dd81-9d6be1255385",
871
+ "colab": {
872
+ "base_uri": "https://localhost:8080/",
873
+ "height": 34
874
+ }
875
+ },
876
+ "source": [
877
+ "tokenizer.encode(\"Mi estas Julien.\").tokens"
878
+ ],
879
+ "execution_count": 0,
880
+ "outputs": [
881
+ {
882
+ "output_type": "execute_result",
883
+ "data": {
884
+ "text/plain": [
885
+ "['<s>', 'Mi', 'Ġestas', 'ĠJuli', 'en', '.', '</s>']"
886
+ ]
887
+ },
888
+ "metadata": {
889
+ "tags": []
890
+ },
891
+ "execution_count": 11
892
+ }
893
+ ]
894
+ },
895
+ {
896
+ "cell_type": "markdown",
897
+ "metadata": {
898
+ "id": "WQpUC_CDhnWW",
899
+ "colab_type": "text"
900
+ },
901
+ "source": [
902
+ "## 3. Train a language model from scratch\n",
903
+ "\n",
904
+ "**Update:** This section follows along the [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py) script, using our new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) directly. Feel free to pick the approach you like best.\n",
905
+ "\n",
906
+ "> We’ll train a RoBERTa-like model, which is a BERT-like with a couple of changes (check the [documentation](https://huggingface.co/transformers/model_doc/roberta.html) for more details).\n",
907
+ "\n",
908
+ "As the model is BERT-like, we’ll train it on a task of *Masked language modeling*, i.e. the predict how to fill arbitrary tokens that we randomly mask in the dataset. This is taken care of by the example script.\n"
909
+ ]
910
+ },
911
+ {
912
+ "cell_type": "code",
913
+ "metadata": {
914
+ "id": "kD140sFjh0LQ",
915
+ "colab_type": "code",
916
+ "outputId": "0bab1f9e-bf7a-4f13-82d3-07fe5866ce78",
917
+ "colab": {
918
+ "base_uri": "https://localhost:8080/",
919
+ "height": 318
920
+ }
921
+ },
922
+ "source": [
923
+ "# Check that we have a GPU\n",
924
+ "!nvidia-smi"
925
+ ],
926
+ "execution_count": 5,
927
+ "outputs": [
928
+ {
929
+ "output_type": "stream",
930
+ "text": [
931
+ "Fri May 15 21:17:12 2020 \n",
932
+ "+-----------------------------------------------------------------------------+\n",
933
+ "| NVIDIA-SMI 440.82 Driver Version: 418.67 CUDA Version: 10.1 |\n",
934
+ "|-------------------------------+----------------------+----------------------+\n",
935
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
936
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
937
+ "|===============================+======================+======================|\n",
938
+ "| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
939
+ "| N/A 38C P0 26W / 250W | 0MiB / 16280MiB | 0% Default |\n",
940
+ "+-------------------------------+----------------------+----------------------+\n",
941
+ " \n",
942
+ "+-----------------------------------------------------------------------------+\n",
943
+ "| Processes: GPU Memory |\n",
944
+ "| GPU PID Type Process name Usage |\n",
945
+ "|=============================================================================|\n",
946
+ "| No running processes found |\n",
947
+ "+-----------------------------------------------------------------------------+\n"
948
+ ],
949
+ "name": "stdout"
950
+ }
951
+ ]
952
+ },
953
+ {
954
+ "cell_type": "code",
955
+ "metadata": {
956
+ "id": "VNZZs-r6iKAV",
957
+ "colab_type": "code",
958
+ "outputId": "c8404d6c-7662-4240-c8da-ee89edfaf51b",
959
+ "colab": {
960
+ "base_uri": "https://localhost:8080/",
961
+ "height": 34
962
+ }
963
+ },
964
+ "source": [
965
+ "# Check that PyTorch sees it\n",
966
+ "import torch\n",
967
+ "torch.cuda.is_available()"
968
+ ],
969
+ "execution_count": 6,
970
+ "outputs": [
971
+ {
972
+ "output_type": "execute_result",
973
+ "data": {
974
+ "text/plain": [
975
+ "True"
976
+ ]
977
+ },
978
+ "metadata": {
979
+ "tags": []
980
+ },
981
+ "execution_count": 6
982
+ }
983
+ ]
984
+ },
985
+ {
986
+ "cell_type": "markdown",
987
+ "metadata": {
988
+ "id": "u0qQzgrBi1OX",
989
+ "colab_type": "text"
990
+ },
991
+ "source": [
992
+ "### We'll define the following config for the model"
993
+ ]
994
+ },
995
+ {
996
+ "cell_type": "code",
997
+ "metadata": {
998
+ "id": "LTXXutqeDzPi",
999
+ "colab_type": "code",
1000
+ "colab": {}
1001
+ },
1002
+ "source": [
1003
+ "from transformers import RobertaConfig\n",
1004
+ "\n",
1005
+ "config = RobertaConfig(\n",
1006
+ " vocab_size=52_000,\n",
1007
+ " max_position_embeddings=514,\n",
1008
+ " num_attention_heads=12,\n",
1009
+ " num_hidden_layers=6,\n",
1010
+ " type_vocab_size=1,\n",
1011
+ ")"
1012
+ ],
1013
+ "execution_count": 0,
1014
+ "outputs": []
1015
+ },
1016
+ {
1017
+ "cell_type": "markdown",
1018
+ "metadata": {
1019
+ "id": "yAwQ82JiE5pi",
1020
+ "colab_type": "text"
1021
+ },
1022
+ "source": [
1023
+ "Now let's re-create our tokenizer in transformers"
1024
+ ]
1025
+ },
1026
+ {
1027
+ "cell_type": "code",
1028
+ "metadata": {
1029
+ "id": "4keFBUjQFOD1",
1030
+ "colab_type": "code",
1031
+ "colab": {}
1032
+ },
1033
+ "source": [
1034
+ "from transformers import RobertaTokenizerFast\n",
1035
+ "\n",
1036
+ "tokenizer = RobertaTokenizerFast.from_pretrained(\"./EsperBERTo\", max_len=512)"
1037
+ ],
1038
+ "execution_count": 0,
1039
+ "outputs": []
1040
+ },
1041
+ {
1042
+ "cell_type": "markdown",
1043
+ "metadata": {
1044
+ "id": "6yNCw-3hFv9h",
1045
+ "colab_type": "text"
1046
+ },
1047
+ "source": [
1048
+ "Finally let's initialize our model.\n",
1049
+ "\n",
1050
+ "**Important:**\n",
1051
+ "\n",
1052
+ "As we are training from scratch, we only initialize from a config, not from an existing pretrained model or checkpoint."
1053
+ ]
1054
+ },
1055
+ {
1056
+ "cell_type": "code",
1057
+ "metadata": {
1058
+ "id": "BzMqR-dzF4Ro",
1059
+ "colab_type": "code",
1060
+ "colab": {}
1061
+ },
1062
+ "source": [
1063
+ "from transformers import RobertaForMaskedLM\n",
1064
+ "\n",
1065
+ "model = RobertaForMaskedLM(config=config)"
1066
+ ],
1067
+ "execution_count": 0,
1068
+ "outputs": []
1069
+ },
1070
+ {
1071
+ "cell_type": "code",
1072
+ "metadata": {
1073
+ "id": "jU6JhBSTKiaM",
1074
+ "colab_type": "code",
1075
+ "outputId": "35879a60-2915-4894-f702-2d649cfa398a",
1076
+ "colab": {
1077
+ "base_uri": "https://localhost:8080/",
1078
+ "height": 34
1079
+ }
1080
+ },
1081
+ "source": [
1082
+ "model.num_parameters()\n",
1083
+ "# => 84 million parameters"
1084
+ ],
1085
+ "execution_count": 10,
1086
+ "outputs": [
1087
+ {
1088
+ "output_type": "execute_result",
1089
+ "data": {
1090
+ "text/plain": [
1091
+ "84095008"
1092
+ ]
1093
+ },
1094
+ "metadata": {
1095
+ "tags": []
1096
+ },
1097
+ "execution_count": 10
1098
+ }
1099
+ ]
1100
+ },
1101
+ {
1102
+ "cell_type": "markdown",
1103
+ "metadata": {
1104
+ "id": "jBtUHRMliOLM",
1105
+ "colab_type": "text"
1106
+ },
1107
+ "source": [
1108
+ "### Now let's build our training Dataset\n",
1109
+ "\n",
1110
+ "We'll build our dataset by applying our tokenizer to our text file.\n",
1111
+ "\n",
1112
+ "Here, as we only have one text file, we don't even need to customize our `Dataset`. We'll just use the `LineByLineDataset` out-of-the-box."
1113
+ ]
1114
+ },
1115
+ {
1116
+ "cell_type": "code",
1117
+ "metadata": {
1118
+ "id": "GlvP_A-THEEl",
1119
+ "colab_type": "code",
1120
+ "outputId": "e0510a33-7937-4a04-fa1c-d4e20b758bb2",
1121
+ "colab": {
1122
+ "base_uri": "https://localhost:8080/",
1123
+ "height": 52
1124
+ }
1125
+ },
1126
+ "source": [
1127
+ "%%time\n",
1128
+ "from transformers import LineByLineTextDataset\n",
1129
+ "\n",
1130
+ "dataset = LineByLineTextDataset(\n",
1131
+ " tokenizer=tokenizer,\n",
1132
+ " file_path=\"./oscar.eo.txt\",\n",
1133
+ " block_size=128,\n",
1134
+ ")"
1135
+ ],
1136
+ "execution_count": 11,
1137
+ "outputs": [
1138
+ {
1139
+ "output_type": "stream",
1140
+ "text": [
1141
+ "CPU times: user 4min 54s, sys: 2.98 s, total: 4min 57s\n",
1142
+ "Wall time: 1min 37s\n"
1143
+ ],
1144
+ "name": "stdout"
1145
+ }
1146
+ ]
1147
+ },
1148
+ {
1149
+ "cell_type": "markdown",
1150
+ "metadata": {
1151
+ "id": "hDLs73HcIHk5",
1152
+ "colab_type": "text"
1153
+ },
1154
+ "source": [
1155
+ "Like in the [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script, we need to define a data_collator.\n",
1156
+ "\n",
1157
+ "This is just a small helper that will help us batch different samples of the dataset together into an object that PyTorch knows how to perform backprop on."
1158
+ ]
1159
+ },
1160
+ {
1161
+ "cell_type": "code",
1162
+ "metadata": {
1163
+ "id": "zTgWPa9Dipk2",
1164
+ "colab_type": "code",
1165
+ "colab": {}
1166
+ },
1167
+ "source": [
1168
+ "from transformers import DataCollatorForLanguageModeling\n",
1169
+ "\n",
1170
+ "data_collator = DataCollatorForLanguageModeling(\n",
1171
+ " tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n",
1172
+ ")"
1173
+ ],
1174
+ "execution_count": 0,
1175
+ "outputs": []
1176
+ },
1177
+ {
1178
+ "cell_type": "markdown",
1179
+ "metadata": {
1180
+ "id": "ri2BIQKqjfHm",
1181
+ "colab_type": "text"
1182
+ },
1183
+ "source": [
1184
+ "### Finally, we are all set to initialize our Trainer"
1185
+ ]
1186
+ },
1187
+ {
1188
+ "cell_type": "code",
1189
+ "metadata": {
1190
+ "id": "YpvnFFmZJD-N",
1191
+ "colab_type": "code",
1192
+ "colab": {}
1193
+ },
1194
+ "source": [
1195
+ "from transformers import Trainer, TrainingArguments\n",
1196
+ "\n",
1197
+ "training_args = TrainingArguments(\n",
1198
+ " output_dir=\"./EsperBERTo\",\n",
1199
+ " overwrite_output_dir=True,\n",
1200
+ " num_train_epochs=1,\n",
1201
+ " per_gpu_train_batch_size=64,\n",
1202
+ " save_steps=10_000,\n",
1203
+ " save_total_limit=2,\n",
1204
+ " prediction_loss_only=True,\n",
1205
+ ")\n",
1206
+ "\n",
1207
+ "trainer = Trainer(\n",
1208
+ " model=model,\n",
1209
+ " args=training_args,\n",
1210
+ " data_collator=data_collator,\n",
1211
+ " train_dataset=dataset,\n",
1212
+ ")"
1213
+ ],
1214
+ "execution_count": 0,
1215
+ "outputs": []
1216
+ },
1217
+ {
1218
+ "cell_type": "markdown",
1219
+ "metadata": {
1220
+ "id": "o6sASa36Nf-N",
1221
+ "colab_type": "text"
1222
+ },
1223
+ "source": [
1224
+ "### Start training"
1225
+ ]
1226
+ },
1227
+ {
1228
+ "cell_type": "code",
1229
+ "metadata": {
1230
+ "id": "VmaHZXzmkNtJ",
1231
+ "colab_type": "code",
1232
+ "outputId": "a19880cb-bcc6-4885-bf24-c2c6d0f56d1e",
1233
+ "colab": {
1234
+ "base_uri": "https://localhost:8080/",
1235
+ "height": 738,
1236
+ "referenced_widgets": [
1237
+ "a58a66392b644b1384661e850c077a6c",
1238
+ "a491e8caa0a048beb3b5259f14eb233f",
1239
+ "837c9ddc3d594e088891874560c646b8",
1240
+ "dbf50873d62c4ba39321faefbed0cca5",
1241
+ "40bf955ba0284e84b198da6be8654219",
1242
+ "fe20a8dae6e84628b5076d02183090f5",
1243
+ "93b3f9eae3cb4e3e859cf456e3547c6d",
1244
+ "6feb10aeb43147e6aba028d065947ae8",
1245
+ "0989d41a4da24e9ebff377e02127642c",
1246
+ "42c6061ef7e44f179db5a6e3551c0f17",
1247
+ "d295dd80550447d88da0f04ce36a22ff",
1248
+ "04e7e6d291da49d5816dc98a2904e95c",
1249
+ "e7d8c3a4fecd40778e32966b29ea65a1",
1250
+ "016d7c8318f742c1943464b08232a510",
1251
+ "8388e9da9da4492c98c19235ca5fc1b5",
1252
+ "39c23c6a972b419eb2eeeebafeaedc22"
1253
+ ]
1254
+ }
1255
+ },
1256
+ "source": [
1257
+ "%%time\n",
1258
+ "trainer.train()"
1259
+ ],
1260
+ "execution_count": 18,
1261
+ "outputs": [
1262
+ {
1263
+ "output_type": "display_data",
1264
+ "data": {
1265
+ "application/vnd.jupyter.widget-view+json": {
1266
+ "model_id": "a58a66392b644b1384661e850c077a6c",
1267
+ "version_minor": 0,
1268
+ "version_major": 2
1269
+ },
1270
+ "text/plain": [
1271
+ "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=1.0, style=ProgressStyle(description_width='i…"
1272
+ ]
1273
+ },
1274
+ "metadata": {
1275
+ "tags": []
1276
+ }
1277
+ },
1278
+ {
1279
+ "output_type": "display_data",
1280
+ "data": {
1281
+ "application/vnd.jupyter.widget-view+json": {
1282
+ "model_id": "0989d41a4da24e9ebff377e02127642c",
1283
+ "version_minor": 0,
1284
+ "version_major": 2
1285
+ },
1286
+ "text/plain": [
1287
+ "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=15228.0, style=ProgressStyle(description_…"
1288
+ ]
1289
+ },
1290
+ "metadata": {
1291
+ "tags": []
1292
+ }
1293
+ },
1294
+ {
1295
+ "output_type": "stream",
1296
+ "text": [
1297
+ "{\"loss\": 7.152712148666382, \"learning_rate\": 4.8358287365379566e-05, \"epoch\": 0.03283425269240872, \"step\": 500}\n",
1298
+ "{\"loss\": 6.928811420440674, \"learning_rate\": 4.671657473075913e-05, \"epoch\": 0.06566850538481744, \"step\": 1000}\n",
1299
+ "{\"loss\": 6.789419063568115, \"learning_rate\": 4.5074862096138694e-05, \"epoch\": 0.09850275807722617, \"step\": 1500}\n",
1300
+ "{\"loss\": 6.688932447433472, \"learning_rate\": 4.343314946151826e-05, \"epoch\": 0.1313370107696349, \"step\": 2000}\n",
1301
+ "{\"loss\": 6.595982004165649, \"learning_rate\": 4.179143682689782e-05, \"epoch\": 0.1641712634620436, \"step\": 2500}\n",
1302
+ "{\"loss\": 6.545944199562073, \"learning_rate\": 4.0149724192277385e-05, \"epoch\": 0.19700551615445233, \"step\": 3000}\n",
1303
+ "{\"loss\": 6.4864857263565066, \"learning_rate\": 3.850801155765695e-05, \"epoch\": 0.22983976884686105, \"step\": 3500}\n",
1304
+ "{\"loss\": 6.412427802085876, \"learning_rate\": 3.686629892303651e-05, \"epoch\": 0.2626740215392698, \"step\": 4000}\n",
1305
+ "{\"loss\": 6.363630670547486, \"learning_rate\": 3.522458628841608e-05, \"epoch\": 0.29550827423167847, \"step\": 4500}\n",
1306
+ "{\"loss\": 6.273832890510559, \"learning_rate\": 3.358287365379564e-05, \"epoch\": 0.3283425269240872, \"step\": 5000}\n",
1307
+ "{\"loss\": 6.197585330963134, \"learning_rate\": 3.1941161019175205e-05, \"epoch\": 0.3611767796164959, \"step\": 5500}\n",
1308
+ "{\"loss\": 6.097779376983643, \"learning_rate\": 3.029944838455477e-05, \"epoch\": 0.39401103230890466, \"step\": 6000}\n",
1309
+ "{\"loss\": 5.985456382751464, \"learning_rate\": 2.8657735749934332e-05, \"epoch\": 0.42684528500131336, \"step\": 6500}\n",
1310
+ "{\"loss\": 5.8448616371154785, \"learning_rate\": 2.70160231153139e-05, \"epoch\": 0.4596795376937221, \"step\": 7000}\n",
1311
+ "{\"loss\": 5.692522863388062, \"learning_rate\": 2.5374310480693457e-05, \"epoch\": 0.4925137903861308, \"step\": 7500}\n",
1312
+ "{\"loss\": 5.562082152366639, \"learning_rate\": 2.3732597846073024e-05, \"epoch\": 0.5253480430785396, \"step\": 8000}\n",
1313
+ "{\"loss\": 5.457240365982056, \"learning_rate\": 2.2090885211452588e-05, \"epoch\": 0.5581822957709482, \"step\": 8500}\n",
1314
+ "{\"loss\": 5.376953645706177, \"learning_rate\": 2.0449172576832152e-05, \"epoch\": 0.5910165484633569, \"step\": 9000}\n",
1315
+ "{\"loss\": 5.298609251022339, \"learning_rate\": 1.8807459942211716e-05, \"epoch\": 0.6238508011557657, \"step\": 9500}\n",
1316
+ "{\"loss\": 5.225468152046203, \"learning_rate\": 1.716574730759128e-05, \"epoch\": 0.6566850538481744, \"step\": 10000}\n",
1317
+ "{\"loss\": 5.174519973754883, \"learning_rate\": 1.5524034672970843e-05, \"epoch\": 0.6895193065405831, \"step\": 10500}\n",
1318
+ "{\"loss\": 5.113943946838379, \"learning_rate\": 1.3882322038350407e-05, \"epoch\": 0.7223535592329918, \"step\": 11000}\n",
1319
+ "{\"loss\": 5.08140989112854, \"learning_rate\": 1.2240609403729971e-05, \"epoch\": 0.7551878119254006, \"step\": 11500}\n",
1320
+ "{\"loss\": 5.072491912841797, \"learning_rate\": 1.0598896769109535e-05, \"epoch\": 0.7880220646178093, \"step\": 12000}\n",
1321
+ "{\"loss\": 5.012459496498108, \"learning_rate\": 8.957184134489099e-06, \"epoch\": 0.820856317310218, \"step\": 12500}\n",
1322
+ "{\"loss\": 4.999591351509094, \"learning_rate\": 7.315471499868663e-06, \"epoch\": 0.8536905700026267, \"step\": 13000}\n",
1323
+ "{\"loss\": 4.994838352203369, \"learning_rate\": 5.673758865248227e-06, \"epoch\": 0.8865248226950354, \"step\": 13500}\n",
1324
+ "{\"loss\": 4.955870885848999, \"learning_rate\": 4.032046230627791e-06, \"epoch\": 0.9193590753874442, \"step\": 14000}\n",
1325
+ "{\"loss\": 4.941655583381653, \"learning_rate\": 2.390333596007355e-06, \"epoch\": 0.9521933280798529, \"step\": 14500}\n",
1326
+ "{\"loss\": 4.931783639907837, \"learning_rate\": 7.486209613869189e-07, \"epoch\": 0.9850275807722616, \"step\": 15000}\n",
1327
+ "\n",
1328
+ "\n",
1329
+ "CPU times: user 1h 43min 36s, sys: 1h 3min 28s, total: 2h 47min 4s\n",
1330
+ "Wall time: 2h 46min 46s\n"
1331
+ ],
1332
+ "name": "stdout"
1333
+ },
1334
+ {
1335
+ "output_type": "execute_result",
1336
+ "data": {
1337
+ "text/plain": [
1338
+ "TrainOutput(global_step=15228, training_loss=5.762423221226405)"
1339
+ ]
1340
+ },
1341
+ "metadata": {
1342
+ "tags": []
1343
+ },
1344
+ "execution_count": 18
1345
+ }
1346
+ ]
1347
+ },
1348
+ {
1349
+ "cell_type": "markdown",
1350
+ "metadata": {
1351
+ "id": "_ZkooHz1-_2h",
1352
+ "colab_type": "text"
1353
+ },
1354
+ "source": [
1355
+ "#### 🎉 Save final model (+ tokenizer + config) to disk"
1356
+ ]
1357
+ },
1358
+ {
1359
+ "cell_type": "code",
1360
+ "metadata": {
1361
+ "id": "QDNgPls7_l13",
1362
+ "colab_type": "code",
1363
+ "colab": {}
1364
+ },
1365
+ "source": [
1366
+ "trainer.save_model(\"./EsperBERTo\")"
1367
+ ],
1368
+ "execution_count": 0,
1369
+ "outputs": []
1370
+ },
1371
+ {
1372
+ "cell_type": "markdown",
1373
+ "metadata": {
1374
+ "id": "d0caceCy_p1-",
1375
+ "colab_type": "text"
1376
+ },
1377
+ "source": [
1378
+ "## 4. Check that the LM actually trained"
1379
+ ]
1380
+ },
1381
+ {
1382
+ "cell_type": "markdown",
1383
+ "metadata": {
1384
+ "id": "iIQJ8ND_AEhl",
1385
+ "colab_type": "text"
1386
+ },
1387
+ "source": [
1388
+ "Aside from looking at the training and eval losses going down, the easiest way to check whether our language model is learning anything interesting is via the `FillMaskPipeline`.\n",
1389
+ "\n",
1390
+ "Pipelines are simple wrappers around tokenizers and models, and the 'fill-mask' one will let you input a sequence containing a masked token (here, `<mask>`) and return a list of the most probable filled sequences, with their probabilities.\n",
1391
+ "\n"
1392
+ ]
1393
+ },
1394
+ {
1395
+ "cell_type": "code",
1396
+ "metadata": {
1397
+ "id": "ltXgXyCbAJLY",
1398
+ "colab_type": "code",
1399
+ "colab": {}
1400
+ },
1401
+ "source": [
1402
+ "from transformers import pipeline\n",
1403
+ "\n",
1404
+ "fill_mask = pipeline(\n",
1405
+ " \"fill-mask\",\n",
1406
+ " model=\"./EsperBERTo\",\n",
1407
+ " tokenizer=\"./EsperBERTo\"\n",
1408
+ ")"
1409
+ ],
1410
+ "execution_count": 0,
1411
+ "outputs": []
1412
+ },
1413
+ {
1414
+ "cell_type": "code",
1415
+ "metadata": {
1416
+ "id": "UIvgZ3S6AO0z",
1417
+ "colab_type": "code",
1418
+ "colab": {
1419
+ "base_uri": "https://localhost:8080/",
1420
+ "height": 283
1421
+ },
1422
+ "outputId": "5f3d2f00-abdc-44a9-9c1b-75e3ec328576"
1423
+ },
1424
+ "source": [
1425
+ "# The sun <mask>.\n",
1426
+ "# =>\n",
1427
+ "\n",
1428
+ "fill_mask(\"La suno <mask>.\")"
1429
+ ],
1430
+ "execution_count": 36,
1431
+ "outputs": [
1432
+ {
1433
+ "output_type": "execute_result",
1434
+ "data": {
1435
+ "text/plain": [
1436
+ "[{'score': 0.02119220793247223,\n",
1437
+ " 'sequence': '<s> La suno estas.</s>',\n",
1438
+ " 'token': 316},\n",
1439
+ " {'score': 0.012403824366629124,\n",
1440
+ " 'sequence': '<s> La suno situas.</s>',\n",
1441
+ " 'token': 2340},\n",
1442
+ " {'score': 0.011061107739806175,\n",
1443
+ " 'sequence': '<s> La suno estis.</s>',\n",
1444
+ " 'token': 394},\n",
1445
+ " {'score': 0.008284995332360268,\n",
1446
+ " 'sequence': '<s> La suno de.</s>',\n",
1447
+ " 'token': 274},\n",
1448
+ " {'score': 0.006471084896475077,\n",
1449
+ " 'sequence': '<s> La suno akvo.</s>',\n",
1450
+ " 'token': 1833}]"
1451
+ ]
1452
+ },
1453
+ "metadata": {
1454
+ "tags": []
1455
+ },
1456
+ "execution_count": 36
1457
+ }
1458
+ ]
1459
+ },
1460
+ {
1461
+ "cell_type": "markdown",
1462
+ "metadata": {
1463
+ "id": "i0qCyyhNAWZi",
1464
+ "colab_type": "text"
1465
+ },
1466
+ "source": [
1467
+ "Ok, simple syntax/grammar works. Let’s try a slightly more interesting prompt:\n",
1468
+ "\n"
1469
+ ]
1470
+ },
1471
+ {
1472
+ "cell_type": "code",
1473
+ "metadata": {
1474
+ "id": "YZ9HSQxAAbme",
1475
+ "colab_type": "code",
1476
+ "colab": {
1477
+ "base_uri": "https://localhost:8080/",
1478
+ "height": 283
1479
+ },
1480
+ "outputId": "aabfeedc-b1d0-4837-b01d-cd42726a5a3d"
1481
+ },
1482
+ "source": [
1483
+ "fill_mask(\"Jen la komenco de bela <mask>.\")\n",
1484
+ "\n",
1485
+ "# This is the beginning of a beautiful <mask>.\n",
1486
+ "# =>"
1487
+ ],
1488
+ "execution_count": 37,
1489
+ "outputs": [
1490
+ {
1491
+ "output_type": "execute_result",
1492
+ "data": {
1493
+ "text/plain": [
1494
+ "[{'score': 0.01814725436270237,\n",
1495
+ " 'sequence': '<s> Jen la komenco de bela urbo.</s>',\n",
1496
+ " 'token': 871},\n",
1497
+ " {'score': 0.015888698399066925,\n",
1498
+ " 'sequence': '<s> Jen la komenco de bela vivo.</s>',\n",
1499
+ " 'token': 1160},\n",
1500
+ " {'score': 0.015662025660276413,\n",
1501
+ " 'sequence': '<s> Jen la komenco de bela tempo.</s>',\n",
1502
+ " 'token': 1021},\n",
1503
+ " {'score': 0.015555007383227348,\n",
1504
+ " 'sequence': '<s> Jen la komenco de bela mondo.</s>',\n",
1505
+ " 'token': 945},\n",
1506
+ " {'score': 0.01412549614906311,\n",
1507
+ " 'sequence': '<s> Jen la komenco de bela tago.</s>',\n",
1508
+ " 'token': 1633}]"
1509
+ ]
1510
+ },
1511
+ "metadata": {
1512
+ "tags": []
1513
+ },
1514
+ "execution_count": 37
1515
+ }
1516
+ ]
1517
+ },
1518
+ {
1519
+ "cell_type": "markdown",
1520
+ "metadata": {
1521
+ "id": "6RsGaD1qAfLP",
1522
+ "colab_type": "text"
1523
+ },
1524
+ "source": [
1525
+ "## 5. Share your model 🎉"
1526
+ ]
1527
+ },
1528
+ {
1529
+ "cell_type": "markdown",
1530
+ "metadata": {
1531
+ "id": "5oESe8djApQw",
1532
+ "colab_type": "text"
1533
+ },
1534
+ "source": [
1535
+ "Finally, when you have a nice model, please think about sharing it with the community:\n",
1536
+ "\n",
1537
+ "- upload your model using the CLI: `transformers-cli upload`\n",
1538
+ "- write a README.md model card and add it to the repository under `model_cards/`. Your model card should ideally include:\n",
1539
+ " - a model description,\n",
1540
+ " - training params (dataset, preprocessing, hyperparameters), \n",
1541
+ " - evaluation results,\n",
1542
+ " - intended uses & limitations\n",
1543
+ " - whatever else is helpful! 🤓\n",
1544
+ "\n",
1545
+ "### **TADA!**\n",
1546
+ "\n",
1547
+ "➡️ Your model has a page on http://huggingface.co/models and everyone can load it using `AutoModel.from_pretrained(\"username/model_name\")`.\n",
1548
+ "\n",
1549
+ "[![tb](https://huggingface.co/blog/assets/01_how-to-train/model_page.png)](https://huggingface.co/julien-c/EsperBERTo-small)\n"
1550
+ ]
1551
+ },
1552
+ {
1553
+ "cell_type": "markdown",
1554
+ "metadata": {
1555
+ "id": "aw9ifsgqBI2o",
1556
+ "colab_type": "text"
1557
+ },
1558
+ "source": [
1559
+ "If you want to take a look at models in different languages, check https://huggingface.co/models\n",
1560
+ "\n",
1561
+ "[![all models](https://huggingface.co/front/thumbnails/models.png)](https://huggingface.co/models)\n"
1562
+ ]
1563
+ }
1564
+ ]
1565
+ }