diff --git a/ch02/01_main-chapter-code/ch02.ipynb b/ch02/01_main-chapter-code/ch02.ipynb index 272e4d769..39c652306 100644 --- a/ch02/01_main-chapter-code/ch02.ipynb +++ b/ch02/01_main-chapter-code/ch02.ipynb @@ -46,8 +46,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch version: 2.5.1\n", - "tiktoken version: 0.7.0\n" + "torch version: 2.9.1\n", + "tiktoken version: 0.12.0\n" ] } ], @@ -103,7 +103,7 @@ "id": "ba08d16f-f237-4166-bf89-0e9fe703e7b4", "metadata": {}, "source": [ - "" + " " ] }, { @@ -120,7 +120,7 @@ "id": "d6b80160-1f10-4aad-a85e-9c79444de9e6", "metadata": {}, "source": [ - "" + " " ] }, { @@ -144,7 +144,7 @@ "id": "09872fdb-9d4e-40c4-949d-52a01a43ec4b", "metadata": {}, "source": [ - "" + " " ] }, { @@ -161,7 +161,18 @@ "execution_count": 2, "id": "40f9d9b1-6d32-485a-825a-a95392a86d79", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nimport os\\nimport urllib.request\\n\\nif not os.path.exists(\"the-verdict.txt\"):\\n url = (\"https://raw.githubusercontent.com/rasbt/\"\\n \"LLMs-from-scratch/main/ch02/01_main-chapter-code/\"\\n \"the-verdict.txt\")\\n file_path = \"the-verdict.txt\"\\n urllib.request.urlretrieve(url, file_path)\\n'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "import requests\n", @@ -391,7 +402,7 @@ "id": "6cbe9330-b587-4262-be9f-497a84ec0e8a", "metadata": {}, "source": [ - "" + " " ] }, { @@ -461,7 +472,7 @@ "id": "177b041d-f739-43b8-bd81-0443ae3a7f8d", "metadata": {}, "source": [ - "" + " " ] }, { @@ -495,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "id": "77d00d96-881f-4691-bb03-84fec2a75a26", "metadata": {}, "outputs": [], @@ -513,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "e1c5de4a-aa4e-4aec-b532-10bb364039d6", "metadata": {}, "outputs": [ @@ -595,7 +606,7 @@ "id": "67407a9f-0202-4e7c-9ed7-1b3154191ebc", "metadata": {}, "source": [ - "" + " " ] }, { @@ -608,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "f531bf46-7c25-4ef8-bff8-0d27518676d5", "metadata": {}, "outputs": [], @@ -619,15 +630,19 @@ " self.int_to_str = {i:s for s,i in vocab.items()}\n", " \n", " def encode(self, text):\n", + " # Getting individual tokens\n", " preprocessed = re.split(r'([,.:;?_!\"()\\']|--|\\s)', text)\n", - " \n", + " \n", + " # Get rid of empty tokens and strip whitespace\n", " preprocessed = [\n", " item.strip() for item in preprocessed if item.strip()\n", " ]\n", + " # Convert to integer IDs\n", " ids = [self.str_to_int[s] for s in preprocessed]\n", " return ids\n", " \n", " def decode(self, ids):\n", + " # Convert integer IDs back to strings\n", " text = \" \".join([self.int_to_str[i] for i in ids])\n", " # Replace spaces before the specified punctuations\n", " text = re.sub(r'\\s+([,.?!\"()\\'])', r'\\1', text)\n", @@ -648,7 +663,7 @@ "id": "cc21d347-ec03-4823-b3d4-9d686e495617", "metadata": {}, "source": [ - "" + " " ] }, { @@ -662,18 +677,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "647364ec-7995-4654-9b4a-7607ccf5f1e4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 56, 2, 850, 988, 602, 533, 746, 5, 1126, 596, 5, 1, 67, 7, 38, 851, 1108, 754, 793, 7]\n" - ] - } - ], + "outputs": [], "source": [ "tokenizer = SimpleTokenizerV1(vocab)\n", "\n", @@ -693,42 +700,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "01d8c8fb-432d-4a49-b332-99f23b233746", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\" It\\' s the last he painted, you know,\" Mrs. Gisburn said with pardonable pride.'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tokenizer.decode(ids)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "54f6aa8b-9827-412e-9035-e827296ab0fe", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\" It\\' s the last he painted, you know,\" Mrs. Gisburn said with pardonable pride.'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tokenizer.decode(tokenizer.encode(text))" ] @@ -754,7 +739,7 @@ "id": "aa7fc96c-e1fd-44fb-b7f5-229d7c7922a4", "metadata": {}, "source": [ - "" + " " ] }, { @@ -802,24 +787,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "d5767eff-440c-4de1-9289-f789349d6b85", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'Hello'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m SimpleTokenizerV1(vocab)\n\u001b[1;32m 3\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello, do you like tea. Is this-- a test?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39mencode(text)\n", - "Cell \u001b[0;32mIn[13], line 12\u001b[0m, in \u001b[0;36mSimpleTokenizerV1.encode\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.:;?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m 9\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 10\u001b[0m item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()\n\u001b[1;32m 11\u001b[0m ]\n\u001b[0;32m---> 12\u001b[0m ids \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstr_to_int[s] \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m preprocessed]\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n", - "Cell \u001b[0;32mIn[13], line 12\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 7\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m([,.:;?_!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m()\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m]|--|\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms)\u001b[39m\u001b[38;5;124m'\u001b[39m, text)\n\u001b[1;32m 9\u001b[0m preprocessed \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 10\u001b[0m item\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m preprocessed \u001b[38;5;28;01mif\u001b[39;00m item\u001b[38;5;241m.\u001b[39mstrip()\n\u001b[1;32m 11\u001b[0m ]\n\u001b[0;32m---> 12\u001b[0m ids \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstr_to_int[s] \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m preprocessed]\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ids\n", - "\u001b[0;31mKeyError\u001b[0m: 'Hello'" - ] - } - ], + "outputs": [], "source": [ "tokenizer = SimpleTokenizerV1(vocab)\n", "\n", @@ -840,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "ce9df29c-6c5b-43f1-8c1a-c7f7b79db78f", "metadata": {}, "outputs": [], @@ -853,43 +824,20 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "57c3143b-e860-4d3b-a22a-de22b547a6a9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1132" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(vocab.items())" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "50e51bb1-ae05-4aa8-a9ff-455b65ed1959", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('younger', 1127)\n", - "('your', 1128)\n", - "('yourself', 1129)\n", - "('<|endoftext|>', 1130)\n", - "('<|unk|>', 1131)\n" - ] - } - ], + "outputs": [], "source": [ "for i, item in enumerate(list(vocab.items())[-5:]):\n", " print(item)" @@ -905,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "948861c5-3f30-4712-a234-725f20d26f68", "metadata": {}, "outputs": [], @@ -943,18 +891,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "4133c502-18ac-4412-9f43-01caf4efa3dc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello, do you like tea? <|endoftext|> In the sunlit terraces of the palace.\n" - ] - } - ], + "outputs": [], "source": [ "tokenizer = SimpleTokenizerV2(vocab)\n", "\n", @@ -968,42 +908,20 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "7ed395fe-dc1b-4ed2-b85b-457cc35aab60", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1131, 5, 355, 1126, 628, 975, 10, 1130, 55, 988, 956, 984, 722, 988, 1131, 7]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tokenizer.encode(text)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "059367f9-7a60-4c0d-8a00-7c4c766d0ebc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'<|unk|>, do you like tea? <|endoftext|> In the sunlit terraces of the <|unk|>.'" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tokenizer.decode(tokenizer.encode(text))" ] @@ -1031,28 +949,20 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "ede1d41f-934b-4bf4-8184-54394a257a94", "metadata": {}, "outputs": [], "source": [ - "# pip install tiktoken" + "!pip install tiktoken --break-system-packages" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "48967a77-7d17-42bf-9e92-fc619d63a59e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tiktoken version: 0.7.0\n" - ] - } - ], + "outputs": [], "source": [ "import importlib\n", "import tiktoken\n", @@ -1062,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "6ad3312f-a5f7-4efc-9d7d-8ea09d7b5128", "metadata": {}, "outputs": [], @@ -1072,18 +982,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "5ff2cd85-7cfb-4325-b390-219938589428", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[15496, 11, 466, 345, 588, 8887, 30, 220, 50256, 554, 262, 4252, 18250, 8812, 2114, 1659, 617, 34680, 27271, 13]\n" - ] - } - ], + "outputs": [], "source": [ "text = (\n", " \"Hello, do you like tea? <|endoftext|> In the sunlit terraces\"\n", @@ -1097,18 +999,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "d26a48bb-f82e-41a8-a955-a1c9cf9d50ab", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello, do you like tea? <|endoftext|> In the sunlit terracesof someunknownPlace.\n" - ] - } - ], + "outputs": [], "source": [ "strings = tokenizer.decode(integers)\n", "\n", @@ -1128,7 +1022,7 @@ "id": "c082d41f-33d7-4827-97d8-993d5a84bb3c", "metadata": {}, "source": [ - "" + " " ] }, { @@ -1152,23 +1046,15 @@ "id": "39fb44f4-0c43-4a6a-9c2f-9cf31452354c", "metadata": {}, "source": [ - "" + " " ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "848d5ade-fd1f-46c3-9e31-1426e315c71b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5145\n" - ] - } - ], + "outputs": [], "source": [ "with open(\"the-verdict.txt\", \"r\", encoding=\"utf-8\") as f:\n", " raw_text = f.read()\n", @@ -1188,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "e84424a7-646d-45b6-99e3-80d15fb761f2", "metadata": {}, "outputs": [], @@ -1198,19 +1084,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "dfbff852-a92f-48c8-a46d-143a0f109f40", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x: [290, 4920, 2241, 287]\n", - "y: [4920, 2241, 287, 257]\n" - ] - } - ], + "outputs": [], "source": [ "context_size = 4\n", "\n", @@ -1231,21 +1108,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "d97b031e-ed55-409d-95f2-aeb38c6fe366", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[290] ----> 4920\n", - "[290, 4920] ----> 2241\n", - "[290, 4920, 2241] ----> 287\n", - "[290, 4920, 2241, 287] ----> 257\n" - ] - } - ], + "outputs": [], "source": [ "for i in range(1, context_size+1):\n", " context = enc_sample[:i]\n", @@ -1256,21 +1122,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "f57bd746-dcbf-4433-8e24-ee213a8c34a1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " and ----> established\n", - " and established ----> himself\n", - " and established himself ----> in\n", - " and established himself in ----> a\n" - ] - } - ], + "outputs": [], "source": [ "for i in range(1, context_size+1):\n", " context = enc_sample[:i]\n", @@ -1298,18 +1153,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "e1770134-e7f3-4725-a679-e04c3be48cac", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PyTorch version: 2.5.1\n" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "print(\"PyTorch version:\", torch.__version__)" @@ -1335,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "74b41073-4c9f-46e2-a1bd-d38e4122b375", "metadata": {}, "outputs": [], @@ -1368,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "5eb30ebe-97b3-43c5-9ff1-a97d621b3c4e", "metadata": {}, "outputs": [], @@ -1405,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "df31d96c-6bfd-4564-a956-6192242d7579", "metadata": {}, "outputs": [], @@ -1416,18 +1263,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "9226d00c-ad9a-4949-a6e4-9afccfc7214f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[tensor([[ 40, 367, 2885, 1464]]), tensor([[ 367, 2885, 1464, 1807]])]\n" - ] - } - ], + "outputs": [], "source": [ "dataloader = create_dataloader_v1(\n", " raw_text, batch_size=1, max_length=4, stride=1, shuffle=False\n", @@ -1440,18 +1279,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "10deb4bc-4de1-4d20-921e-4b1c7a0e1a6d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[tensor([[ 367, 2885, 1464, 1807]]), tensor([[2885, 1464, 1807, 3619]])]\n" - ] - } - ], + "outputs": [], "source": [ "second_batch = next(data_iter)\n", "print(second_batch)" @@ -1484,36 +1315,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "1916e7a6-f03d-4f09-91a6-d0bdbac5a58c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inputs:\n", - " tensor([[ 40, 367, 2885, 1464],\n", - " [ 1807, 3619, 402, 271],\n", - " [10899, 2138, 257, 7026],\n", - " [15632, 438, 2016, 257],\n", - " [ 922, 5891, 1576, 438],\n", - " [ 568, 340, 373, 645],\n", - " [ 1049, 5975, 284, 502],\n", - " [ 284, 3285, 326, 11]])\n", - "\n", - "Targets:\n", - " tensor([[ 367, 2885, 1464, 1807],\n", - " [ 3619, 402, 271, 10899],\n", - " [ 2138, 257, 7026, 15632],\n", - " [ 438, 2016, 257, 922],\n", - " [ 5891, 1576, 438, 568],\n", - " [ 340, 373, 645, 1049],\n", - " [ 5975, 284, 502, 284],\n", - " [ 3285, 326, 11, 287]])\n" - ] - } - ], + "outputs": [], "source": [ "dataloader = create_dataloader_v1(raw_text, batch_size=8, max_length=4, stride=4, shuffle=False)\n", "\n", @@ -1559,7 +1364,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "15a6304c-9474-4470-b85d-3991a49fa653", "metadata": {}, "outputs": [], @@ -1577,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "93cb2cee-9aa6-4bb8-8977-c65661d16eda", "metadata": {}, "outputs": [], @@ -1599,24 +1404,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "a686eb61-e737-4351-8f1c-222913d47468", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter containing:\n", - "tensor([[ 0.3374, -0.1778, -0.1690],\n", - " [ 0.9178, 1.5810, 1.3010],\n", - " [ 1.2753, -0.2010, -0.1606],\n", - " [-0.4015, 0.9666, -1.1481],\n", - " [-1.1589, 0.3255, -0.6315],\n", - " [-2.8400, -0.7849, -1.4096]], requires_grad=True)\n" - ] - } - ], + "outputs": [], "source": [ "print(embedding_layer.weight)" ] @@ -1640,18 +1431,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "e43600ba-f287-4746-8ddf-d0f71a9023ca", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-0.4015, 0.9666, -1.1481]], grad_fn=)\n" - ] - } - ], + "outputs": [], "source": [ "print(embedding_layer(torch.tensor([3])))" ] @@ -1667,21 +1450,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "50280ead-0363-44c8-8c35-bb885d92c8b7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[ 1.2753, -0.2010, -0.1606],\n", - " [-0.4015, 0.9666, -1.1481],\n", - " [-2.8400, -0.7849, -1.4096],\n", - " [ 0.9178, 1.5810, 1.3010]], grad_fn=)\n" - ] - } - ], + "outputs": [], "source": [ "print(embedding_layer(input_ids))" ] @@ -1761,7 +1533,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "0b9e344d-03a6-4f2c-b723-67b6a20c5041", "metadata": {}, "outputs": [], @@ -1783,7 +1555,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "ad56a263-3d2e-4d91-98bf-d0b68d3c7fc3", "metadata": {}, "outputs": [], @@ -1799,29 +1571,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "84416b60-3707-4370-bcbc-da0b62f2b64d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Token IDs:\n", - " tensor([[ 40, 367, 2885, 1464],\n", - " [ 1807, 3619, 402, 271],\n", - " [10899, 2138, 257, 7026],\n", - " [15632, 438, 2016, 257],\n", - " [ 922, 5891, 1576, 438],\n", - " [ 568, 340, 373, 645],\n", - " [ 1049, 5975, 284, 502],\n", - " [ 284, 3285, 326, 11]])\n", - "\n", - "Inputs shape:\n", - " torch.Size([8, 4])\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Token IDs:\\n\", inputs)\n", "print(\"\\nInputs shape:\\n\", inputs.shape)" @@ -1829,18 +1582,10 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "7766ec38-30d0-4128-8c31-f49f063c43d1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([8, 4, 256])\n" - ] - } - ], + "outputs": [], "source": [ "token_embeddings = token_embedding_layer(inputs)\n", "print(token_embeddings.shape)\n", @@ -1859,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "cc048e20-7ac8-417e-81f5-8fe6f9a4fe07", "metadata": {}, "outputs": [], @@ -1873,18 +1618,10 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "c369a1e7-d566-4b53-b398-d6adafb44105", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([4, 256])\n" - ] - } - ], + "outputs": [], "source": [ "pos_embeddings = pos_embedding_layer(torch.arange(max_length))\n", "print(pos_embeddings.shape)\n", @@ -1903,18 +1640,10 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "b22fab89-526e-43c8-9035-5b7018e34288", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([8, 4, 256])\n" - ] - } - ], + "outputs": [], "source": [ "input_embeddings = token_embeddings + pos_embeddings\n", "print(input_embeddings.shape)\n", @@ -1963,7 +1692,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1977,7 +1706,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/language_modeling_from_scratch.ipynb b/examples/language_modeling_from_scratch.ipynb new file mode 100644 index 000000000..c64b52020 --- /dev/null +++ b/examples/language_modeling_from_scratch.ipynb @@ -0,0 +1,8784 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X4cRE8IbIrIV" + }, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "MOsHUjgdIrIW" + }, + "outputs": [], + "source": [ + "#! pip install datasets transformers" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I7ixNXAJYUFg" + }, + "source": [ + "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n", + "\n", + "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n", + "\n", + "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your username and password:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "kGzVI-a3YUFh", + "outputId": "c6ecca88-16e2-45e5-8e36-6e4fa694ce70", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "1873aedecad04c23b6e3ba5ee9d0f290", + "c050dcc2d63941cfa098f632defe5259", + "8670dd7ef35342a38a0943cb0b16a83c", + "f9c72afa756b4cb3aa390bc05b3ab879", + "739dae4db3234b3b91703532fbf8d889", + "9055320a8a1a48918ba78916f1e01ede", + "25e0a35bef1b4b4183bc1f6119f244fb", + "bca2ac9121f742359f6710632ff6c532", + "6326b94938684ff492a131b31bc4e300", + "ce147eb276cb44a7aac9b21ba2c94394", + "9c01006bb300401c8a965db4f8ec8401", + "3ebeb0ba100544e1ae85a40e51751bee", + "15dded78fb4f40a7ae1812386f08d037", + "5912e75efc374dbeb6a450d20185c904", + "9d4aff0aa0984e7aa1a5fc6b072e67e4", + "9d9e4f7fedea4f73b999fecc069a62b2", + "447517e5fcc04881868189d8a1f1036f", + "1ce4bd56762540a9893b1d116de7c4e9", + "fc65438502cd47eeace0150b9e06c6ba", + "9848260101c5420eb9023cfedf3d5154" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
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text
0Galveston Ballet \\n
1On October 13 , 2009 , Dylan released a Christmas album , Christmas in the Heart , comprising such Christmas standards as \" Little Drummer Boy \" , \" Winter Wonderland \" and \" Here Comes Santa Claus \" . Dylan 's royalties from the sale of this album will benefit the charities Feeding America in the USA , Crisis in the UK , and the World Food Programme . \\n
2Corn crakes are the subject of three stanzas of the seventeenth century poet Andrew Marvell 's \" Upon Appleton House \" , written in 1651 about the North Yorkshire country estate of Thomas Fairfax . The narrator depicts the scene of a mower cutting the grass , before his \" whistling Sithe \" unknowingly \" carves the Rail \" . The farmhand draws out the scythe \" all bloody from its breast \" and \" does the stroke detest \" . It continues with a stanza that demonstrates the problematic nature of the corn crake 's nesting habits : \\n
3
4In 1945 , the USACE began the Missouri River Bank Stabilization and Navigation Project , which would permanently increase the river 's navigation channel to a width of 300 feet ( 91 m ) and a depth of nine feet ( 2 @.@ 7 m ) . During work that continues to this day , the 735 @-@ mile ( 1 @,@ 183 km ) navigation channel from Sioux City to St. Louis has been controlled by building rock dikes to direct the river 's flow and scour out sediments , sealing and cutting off meanders and side channels , and dredging the riverbed . However , the Missouri has often resisted the efforts of the USACE to control its depth . In 2006 , several U.S. Coast Guard boats ran aground in the Missouri River because the navigation channel had been severely silted . The USACE was blamed for failing to maintain the channel to the minimum depth . \\n
5DuMont produced more than 20 @,@ 000 television episodes during the decade from 1946 to 1956 . Because the shows were created prior to the launch of Ampex 's electronic videotape recorder in late 1956 , all of them were initially broadcast live in black and white , then recorded on film kinescope for reruns and for West Coast rebroadcasts . By the early 1970s , their vast library of 35mm and 16mm kinescopes eventually wound up in the hands of \" a successor network , \" who reportedly disposed of all of them in New York City 's East River to make room for more recent @-@ vintage videotapes in a warehouse . Other kinescopes were put through a silver reclaiming process , because of the microscopic amounts of silver that made up the emulsion of black @-@ and @-@ white film during this time . It is estimated that only about 350 complete DuMont television shows survive today , the most famous being virtually all of Jackie Gleason 's Honeymooners comedy sketches . Most of the existing episodes are believed to have come from the personal archives of DuMont 's hosts , such as Gleason and Dennis James . \\n
6Kosmoceratops - ( Utah , USA ) \\n
7
8
9Nathan had a strong first season with the Rangers , as he was selected to the represent the Rangers at the 2012 MLB All Star Game , the fifth all star selection of his career . He finished his 2012 campaign with 37 saves and an ERA of 2 @.@ 80 . During a game against the Tampa Bay Rays on April 8 , 2013 , Nathan earned his 300th career save after striking out Ben Zobrist looking on a controversial strike call made by home plate umpire Marty Foster . TV cameras captured Nathan saying \" Wow ! \" after the call . \\n
" + ] + }, + "metadata": {} + } + ], + "source": [ + "show_random_elements(datasets[\"train\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CKerdF353l-o" + }, + "source": [ + "As we can see, some of the texts are a full paragraph of a Wikipedia article while others are just titles or empty lines." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JEA1ju653l-p" + }, + "source": [ + "## Causal Language modeling" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v5GTGKZS3l-q" + }, + "source": [ + "For causal language modeling (CLM) we are going to take all the texts in our dataset and concatenate them after they are tokenized. Then we will split them in examples of a certain sequence length. This way the model will receive chunks of contiguous text that may look like:\n", + "```\n", + "part of text 1\n", + "```\n", + "or\n", + "```\n", + "end of text 1 [BOS_TOKEN] beginning of text 2\n", + "```\n", + "depending on whether they span over several of the original texts in the dataset or not. The labels will be the same as the inputs, shifted to the left.\n", + "\n", + "We will use the [`gpt2`](https://huggingface.co/gpt2) architecture for this example. You can pick any of the checkpoints listed [here](https://huggingface.co/models?filter=causal-lm) instead. For the tokenizer, you can replace the checkpoint by the one you trained yourself." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "-WGBCO343l-q" + }, + "outputs": [], + "source": [ + "model_checkpoint = \"gpt2\"\n", + "tokenizer_checkpoint = \"sgugger/gpt2-like-tokenizer\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5io6fY_d3l-u" + }, + "source": [ + "To tokenize all our texts with the same vocabulary that was used when training the model, we have to download a pretrained tokenizer. This is all done by the `AutoTokenizer` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "iAYlS40Z3l-v", + "outputId": "8fc33ecf-e16d-4f67-cc5e-7ffb642229ed", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "1983e93246754863980182259912d637", + "492ffb3dd7a247cf8ff6b325a53fd122", + "67cad1034d644afbb15460b78ca066c3", + "6e2f685b538d44929923d6f9101f2545", + "31e3f19714c14073ad49f124066d71e7", + "8ec183a90b014d719a2997e026ca2b87", + "ae16b187560f47119b7e599372e10059", + "115379373b3e4215a4d0d9cc68820de2", + "b6f9298e9fb149f18681548f2ff376f4", + "033f361002dc48e9bd8134020b5c5396", + "5fbb9d64dc214f14bf8e3285e08e01a2", + "a57d89ac61024608b889ae961ba0cd9f", + "033f386bb2ce47098bdb9397c91a1d69", + "b9474c07263e4a25a80fd2039a4c4de0", + "051960acf13e43379afb2cb41dbad304", + "3b2c5e34dc9c4148a01a3245374bf525", + "2f6add95a9b24e9db7024a3298a50c0e", + "e403848733ff410ba674ee28424fa1e8", + "5cc827da2dcc4cd08ee365e7e236eaed", + "b2394a7c82a146288a802774fa2e425a", + "c0febbfa3a8c4b428611ee241e783042", + "0d611c5b2a334e4084fef2f0105296b0", + "37c4695912dc4146ae1c3c23299311b2", + "89f750c392c446dab817ca4e959f2035", + "9c7478fc3e29404ca0e4b64f10210eff", + "79237554092a40019229058764663a5f", + "52b438a946504b39bc532ef94589103f", + "e6108bd151b84c649eabc13f4830c61b", + "06fce2a07b7543e69e43aa355212095a", + "a9d2e7030d174c5d95de392d908d5cad", + "fd588b478b25414da9cc2a6d87d4aebc", + "0ea7d55b28f744bbb759661b71f4572c", + "b44c5fe4223448b8a36c8346d8af1177", + "7088c695cae84e4a90cc93d8649fee96", + "4a98cd00169d4a4ba4c31c934ad140f2", + "f3f2ef9864f5446081a61a29714fc230", + "f1564f95dcbd42bba6b5e4f2e56f6b5b", + "3551d5a3af794fb88d52b6d4ecde6ab8", + "877cf4e64e564f92861b5f9612ee0f8c", + "f8448486ed004dd59198dbea13a22802", + "1ae3984dd77e406bb04d31f88d174efc", + "42e13d7df6c84da0968f5c4be67d07d9", + "32bba17550814c5db294119d1269ca70", + "85ac77419fb24cf482e56afb2a6229f6", + "eb481510836e416d91f71c8dd9a02cfc", + "70827641e13944d692f63b0aba27f796", + "2ddd65f23b934c7f92b154729c307b43", + "20e3579687ef4bf4a68b05e7328d1023", + "01ce0a761af34967a3fc5e6b6847156c", + "6a07abf9fd894a82ba69815fb1d9a141", + "162048bd5cc54c1eb9387fc18f128711", + "7b15dd386ffd48e4bb34394943bbe7d2", + "73e8dfa38cdf4d2eaad4271091d28742", + "2d945cb6a79040bfb4d378ca87b1c740", + "f424f241a6a140fca90cf8f555759b6d" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/236 [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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