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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['lets', '##s', 'try', 'to', 'token', '##ize']\n"
]
}
],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
"tokens = tokenizer.tokenize('''Letss try to tokenize''')\n",
"print(tokens)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c2fdecef86644ec1b3467bf653e8d30d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/684 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a4c6b7d714c40ca9695acf581de7bb2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)ve/main/spiece.model: 0%| | 0.00/760k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "49267a76ecfc4aee9d4906e96ddbca5b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/1.31M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['▁let', \"'\", 's', '▁learn', '▁to', '▁code', '▁in', '▁hugging', 'face']\n"
]
}
],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"albert_tokenizer = AutoTokenizer.from_pretrained('albert-base-v2')\n",
"tokens = albert_tokenizer.tokenize('''Let's learn to code in huggingface''')\n",
"print(tokens)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['these', 'are', 'broken', 'down', 'into', 'token', '##s']\n",
"[2122, 2024, 3714, 2091, 2046, 19204, 2015]\n"
]
}
],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokeninzer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
"tokens = tokenizer.tokenize('These are broken down into tokens')\n",
"print(tokens)\n",
"input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
"print(input_ids)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['this', 'is', 'me', 'practicing']\n",
"[2023, 2003, 2033, 12560]\n",
"['this', 'is', 'me', 'practicing']\n"
]
}
],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
"tokens = tokenizer.tokenize('This is me practicing')\n",
"print(tokens)\n",
"\n",
"input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
"print(input_ids)\n",
"\n",
"tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
"print(tokens)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['this', 'is', 'me', 'practicing']\n",
"[2023, 2003, 2033, 12560]\n",
"['this', 'is', 'me', 'practicing']\n",
"this is me practicing\n"
]
}
],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
"tokens = tokenizer.tokenize('This is me practicing')\n",
"print(tokens)\n",
"input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
"print(input_ids)\n",
"tokens_2 = tokenizer.convert_ids_to_tokens(input_ids)\n",
"print(tokens_2)\n",
"strings = tokenizer.convert_tokens_to_string(tokens)\n",
"print(strings)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'input_ids': [101, 2023, 2003, 2033, 12560, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1]}\n"
]
}
],
"source": [
"final_ids = tokenizer.prepare_for_model(input_ids)\n",
"print(final_ids)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['this', 'is', 'me', 'practicing', 'the', 'use', 'of', 'auto', '##tok', '##eni', '##zer']\n",
"[2023, 2003, 2033, 12560, 1996, 2224, 1997, 8285, 18715, 18595, 6290]\n",
"{'input_ids': [101, 2023, 2003, 2033, 12560, 1996, 2224, 1997, 8285, 18715, 18595, 6290, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
"[CLS] this is me practicing the use of autotokenizer [SEP]\n"
]
}
],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"sentence = 'This is me practicing the use of AutoTokenizer'\n",
"tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
"tokens = tokenizer.tokenize(sentence)\n",
"print(tokens)\n",
"input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
"print(input_ids)\n",
"inputs = tokenizer.prepare_for_model(input_ids)\n",
"print(inputs)\n",
"\n",
"decode = tokenizer.decode(inputs['input_ids'])\n",
"print(decode)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
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