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@Arunprakash-A
Created July 10, 2024 16:51
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indictrans2-indic-en-1B.ipynb
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"cells": [
{
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"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Arunprakash-A/158d2385ee1a9853afdb9fb3ce44e9c6/indictrans2-indic-en-1b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Install External Dependencies\n",
"* We do not need a GPU runtime to run this notebook.\n",
"* First, let's install some external dependencies"
],
"metadata": {
"id": "uvBUdUhna7UU"
}
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"!git clone https://github.com/VarunGumma/IndicTransTokenizer\n",
"!cd IndicTransTokenizer/ && pip install --editable ./"
],
"metadata": {
"id": "21P5OA0WsFGM"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"* Now restart the session.\n",
"* Click \"Runtime\" and click on \"Restart session\".\n",
"* Once you connect back to the machine, you can start executing the cells below.\n"
],
"metadata": {
"id": "pyom0vRtbb14"
}
},
{
"cell_type": "markdown",
"source": [
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"* Import the required packages"
],
"metadata": {
"id": "m0yNwoNfa6HG"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "UexlcYQ3rivc"
},
"outputs": [],
"source": [
"import torch\n",
"from transformers import (\n",
" AutoModelForSeq2SeqLM,\n",
" AutoTokenizer,\n",
")\n",
"from IndicTransTokenizer import IndicProcessor"
]
},
{
"cell_type": "code",
"source": [
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],
"metadata": {
"id": "l3b9oVh2jjUK"
},
"execution_count": 2,
"outputs": []
},
{
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"source": [
"# Load The Tokenizer and the Model\n",
"\n",
" * Now we load the tokenizer, the model and learned weights by mentioning \"ai4bharat/indictrans2-en-indic-1B\" as a model_name in `AutoTokenizer` and `AutoModelForSeq2SeqLM` classes\n",
"\n",
" * It will load all the required files and make the model ready for inference.\n"
],
"metadata": {
"id": "pqmceSEhcRLG"
}
},
{
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"model_name = \"ai4bharat/indictrans2-indic-en-1B\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True)\n",
"model.to(DEVICE)\n",
"\n",
"ip = IndicProcessor(inference=True)"
],
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},
"id": "PY3BVWShbShA",
"outputId": "34cc529e-6f46-4bee-adde-963598cfa14a"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/1.10k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "797504e09be1498e9afb8c8e285a8ad8"
}
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"model_id": "bfefca7b1c5d407d98b2e76883117138"
}
},
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"A new version of the following files was downloaded from https://huggingface.co/ai4bharat/indictrans2-indic-en-1B:\n",
"- tokenization_indictrans.py\n",
". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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"version_major": 2,
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{
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"version_major": 2,
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"A new version of the following files was downloaded from https://huggingface.co/ai4bharat/indictrans2-indic-en-1B:\n",
"- configuration_indictrans.py\n",
". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"A new version of the following files was downloaded from https://huggingface.co/ai4bharat/indictrans2-indic-en-1B:\n",
"- modeling_indictrans.py\n",
". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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"metadata": {}
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]
},
{
"cell_type": "markdown",
"source": [
"# Prepare Inputs"
],
"metadata": {
"id": "C0XyEHCIhI7k"
}
},
{
"cell_type": "code",
"source": [
"input_sentences = [\n",
" \"जब मैं छोटा था, मैं हर रोज़ पार्क जाता था।\",\n",
" \"हमने पिछले सप्ताह एक नई फिल्म देखी जो कि बहुत प्रेरणादायक थी।\",\n",
" \"अगर तुम मुझे उस समय पास मिलते, तो हम बाहर खाना खाने चलते।\",\n",
" \"मेरे मित्र ने मुझे उसके जन्मदिन की पार्टी में बुलाया है, और मैं उसे एक तोहफा दूंगा।\",\n",
"]\n",
"src_lang, tgt_lang = \"hin_Deva\", \"eng_Latn\"\n",
"\n",
"batch = ip.preprocess_batch(\n",
" input_sentences,\n",
" src_lang=src_lang,\n",
" tgt_lang=tgt_lang,\n",
")"
],
"metadata": {
"id": "3Bs4b7p3dnxQ"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"* Pass it through the tokenizer"
],
"metadata": {
"id": "kJWK67LNjcRv"
}
},
{
"cell_type": "code",
"source": [
"# Tokenize the sentences and generate input encodings\n",
"inputs = tokenizer(\n",
" batch,\n",
" truncation=True,\n",
" padding=\"longest\",\n",
" return_tensors=\"pt\",\n",
" return_attention_mask=True,\n",
").to(DEVICE)"
],
"metadata": {
"id": "U4cFc2tDjT_P"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Do Inference"
],
"metadata": {
"id": "B8t9W31Ajwww"
}
},
{
"cell_type": "code",
"source": [
"# run the model in inference mode\n",
"with torch.no_grad():\n",
"\n",
" generated_tokens = model.generate(\n",
" **inputs,\n",
" use_cache=True,\n",
" min_length=0,\n",
" max_length=256,\n",
" num_beams=5,\n",
" num_return_sequences=1,\n",
" ).to(DEVICE)"
],
"metadata": {
"id": "vamruPDJjse9"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Decode the output tokens"
],
"metadata": {
"id": "YoJTbmpbkJFS"
}
},
{
"cell_type": "code",
"source": [
"with tokenizer.as_target_tokenizer():\n",
" generated_tokens = tokenizer.batch_decode(\n",
" generated_tokens.detach().cpu().tolist(),\n",
" skip_special_tokens=True,\n",
" clean_up_tokenization_spaces=True,\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9h34541kj9Kk",
"outputId": "38aadb1f-dee0-4fd3-d414-fffd39296d36"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3946: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Display the translated text"
],
"metadata": {
"id": "hvxD0qW-kcD8"
}
},
{
"cell_type": "code",
"source": [
"# Postprocess the translations, including entity replacement\n",
"translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang)\n",
"\n",
"for input_sentence, translation in zip(input_sentences, translations):\n",
" print(f\"{src_lang}: {input_sentence}\")\n",
" print(f\"{tgt_lang}: {translation}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vd5OURbnkQ7Y",
"outputId": "9281c052-9c33-4edf-940c-bb8118c79311"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"hin_Deva: जब मैं छोटा था, मैं हर रोज़ पार्क जाता था।\n",
"eng_Latn: When I was young, I used to go to the park every day.\n",
"hin_Deva: हमने पिछले सप्ताह एक नई फिल्म देखी जो कि बहुत प्रेरणादायक थी।\n",
"eng_Latn: We saw a new movie last week that was very inspiring.\n",
"hin_Deva: अगर तुम मुझे उस समय पास मिलते, तो हम बाहर खाना खाने चलते।\n",
"eng_Latn: If you'd given me a pass at that time, we'd have gone out to eat.\n",
"hin_Deva: मेरे मित्र ने मुझे उसके जन्मदिन की पार्टी में बुलाया है, और मैं उसे एक तोहफा दूंगा।\n",
"eng_Latn: My friend has invited me to her birthday party, and I'll give her a present.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Conclusion\n",
"\n",
" * We have successfully run the model.\n",
" * For more details, visit the github repo:https://github.com/AI4Bharat/IndicTrans2/tree/main/huggingface_interface\n",
" * If the code breaks, feel free to reach us at: mail"
],
"metadata": {
"id": "x-97zvGQko0m"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Dkmu3EmCkmeF"
},
"execution_count": null,
"outputs": []
}
]
}
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