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indictrans2_en_indic_1b_.ipynb
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{
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"* We do not need GPU runtime for this notebook.\n",
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],
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}
},
{
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],
"metadata": {
"id": "21P5OA0WsFGM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"* Now restart the session.\n",
"* If you are running this notebook in colab, click \"Runtime\" and click on \"Restart session\".\n"
],
"metadata": {
"id": "pyom0vRtbb14"
}
},
{
"cell_type": "markdown",
"source": [
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"* Import the required packages"
],
"metadata": {
"id": "m0yNwoNfa6HG"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UexlcYQ3rivc"
},
"outputs": [],
"source": [
"import torch\n",
"from transformers import (\n",
" AutoModelForSeq2SeqLM,\n",
" AutoTokenizer,\n",
")\n",
"from IndicTransTokenizer import IndicProcessor"
]
},
{
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],
"metadata": {
"id": "l3b9oVh2jjUK"
},
"execution_count": null,
"outputs": []
},
{
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"# 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"
],
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"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True)\n",
"\n",
"ip = IndicProcessor(inference=True)"
],
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},
"id": "PY3BVWShbShA",
"outputId": "13cbd80b-71b7-4d49-a65b-5cc49874f556"
},
"execution_count": null,
"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": "7cb3e4b45d794e2eaa5efb8e6528089e"
}
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"model_id": "8dcd6cd9e81c4041839c682ba2726c58"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"A new version of the following files was downloaded from https://huggingface.co/ai4bharat/indictrans2-en-indic-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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{
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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-en-indic-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": {
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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-en-indic-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",
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]
},
{
"cell_type": "markdown",
"source": [
"# Prepare Inputs"
],
"metadata": {
"id": "C0XyEHCIhI7k"
}
},
{
"cell_type": "code",
"source": [
"input_sentences = [\n",
" \"When I was young, I used to go to the park every day.\",\n",
" \"We watched a new movie last week, which was very inspiring.\",\n",
" \"If you had met me at that time, we would have gone out to eat.\",\n",
" \"My friend has invited me to his birthday party, and I will give him a gift.\",\n",
"]\n",
"\n",
"src_lang, tgt_lang = \"eng_Latn\", \"hin_Deva\"\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": null,
"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": null,
"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",
" )"
],
"metadata": {
"id": "vamruPDJjse9"
},
"execution_count": null,
"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": "6dc5da76-fca2-488c-b5a5-3467c8728934"
},
"execution_count": null,
"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": "bd66b15d-6356-4de2-a1b1-48d489188e1f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"eng_Latn: When I was young, I used to go to the park every day.\n",
"hin_Deva: जब मैं छोटा था, मैं हर दिन पार्क जाता था। \n",
"eng_Latn: We watched a new movie last week, which was very inspiring.\n",
"hin_Deva: हमने पिछले हफ्ते एक नई फिल्म देखी, जो बहुत प्रेरणादायक थी। \n",
"eng_Latn: If you had met me at that time, we would have gone out to eat.\n",
"hin_Deva: अगर आप उस समय मुझसे मिलते तो हम बाहर खाना खाने जाते। \n",
"eng_Latn: My friend has invited me to his birthday party, and I will give him a gift.\n",
"hin_Deva: मेरे दोस्त ने मुझे अपने जन्मदिन की पार्टी में आमंत्रित किया है, और मैं उसे एक उपहार दूंगा। \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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