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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install datasets transformers[sentencepiece] sacrebleu -q"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lkCfU5j5J-tA",
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+ "name": "stdout",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.1/104.1 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
+ ]
+ }
+ ]
+ },
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+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import sys\n",
+ "import transformers\n",
+ "import tensorflow as tf\n",
+ "from datasets import load_dataset\n",
+ "from transformers import AutoTokenizer\n",
+ "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
+ "from transformers import AdamWeightDecay\n",
+ "from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM"
+ ],
+ "metadata": {
+ "id": "rsLOz5efKE1q"
+ },
+ "execution_count": 4,
+ "outputs": []
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+ "model_checkpoint = \"Helsinki-NLP/opus-mt-en-hi\""
+ ],
+ "metadata": {
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+ },
+ "execution_count": 5,
+ "outputs": []
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+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
+ "Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
+ "You are not authenticated with the Hugging Face Hub in this notebook.\n",
+ "If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "README.md: 0%| | 0.00/3.14k [00:00, ?B/s]"
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+ "application/vnd.jupyter.widget-view+json": {
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+ "metadata": {}
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+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/1659083 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating validation split: 0%| | 0/520 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating test split: 0%| | 0/2507 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "raw_datasets"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gmENdJTjKRuW",
+ "outputId": "d241960a-d331-423e-9ccc-95929caa0e78"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['translation'],\n",
+ " num_rows: 1659083\n",
+ " })\n",
+ " validation: Dataset({\n",
+ " features: ['translation'],\n",
+ " num_rows: 520\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['translation'],\n",
+ " num_rows: 2507\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "raw_datasets['train'][1]"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wqqwqb5NKZsJ",
+ "outputId": "13917ce3-f4f1-46a8-c753-f2cf33872756"
+ },
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'translation': {'en': 'Accerciser Accessibility Explorer',\n",
+ " 'hi': 'एक्सेर्साइसर पहुंचनीयता अन्वेषक'}}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#Preprocessing The Data\n",
+ "---"
+ ],
+ "metadata": {
+ "id": "fGTXPG0oKega"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 232,
+ "referenced_widgets": [
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+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/44.0 [00:00, ?B/s]"
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+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ }
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+ },
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+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "vocab.json: 0%| | 0.00/2.10M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
+ " warnings.warn(\"Recommended: pip install sacremoses.\")\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenizer(\"Hello, this is a sentence!\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gAHQj2djKvO1",
+ "outputId": "82074e9d-5c51-4397-eda3-0b2150d8ed45"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'input_ids': [12110, 2, 90, 23, 19, 8800, 61, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenizer([\"Hello, this is a sentence!\", \"This is another sentence.\"])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZIQnIKhLK5KL",
+ "outputId": "e8e6fb3d-aebb-4bb8-9006-0f32f76a9c7e"
+ },
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'input_ids': [[12110, 2, 90, 23, 19, 8800, 61, 0], [239, 23, 414, 8800, 3, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with tokenizer.as_target_tokenizer():\n",
+ " print(tokenizer([\"एक्सेर्साइसर पहुंचनीयता अन्वेषक\"]))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cM4osp_pK9WA",
+ "outputId": "cca1948f-c726-4c8a-ef46-d4de57962592"
+ },
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'input_ids': [[26618, 16155, 346, 33383, 0]], 'attention_mask': [[1, 1, 1, 1, 1]]}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/transformers/tokenization_utils_base.py:3953: 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": "code",
+ "source": [
+ "\n",
+ "max_input_length = 128\n",
+ "max_target_length = 128\n",
+ "\n",
+ "source_lang = \"en\"\n",
+ "target_lang = \"hi\"\n",
+ "\n",
+ "\n",
+ "def preprocess_function(examples):\n",
+ " inputs = [ex[source_lang] for ex in examples[\"translation\"]]\n",
+ " targets = [ex[target_lang] for ex in examples[\"translation\"]]\n",
+ " model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
+ "\n",
+ " # Setup the tokenizer for targets\n",
+ " with tokenizer.as_target_tokenizer():\n",
+ " labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
+ "\n",
+ " model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
+ " return model_inputs"
+ ],
+ "metadata": {
+ "id": "DcSfZOlVLAt_"
+ },
+ "execution_count": 13,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "preprocess_function(raw_datasets[\"train\"][:2])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Kv0Y9UNZLGPy",
+ "outputId": "fdeb90a6-c8fb-45b0-bc02-1f99efd0eebb"
+ },
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'input_ids': [[3872, 85, 2501, 132, 15441, 36398, 0], [32643, 28541, 36253, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1]], 'labels': [[63, 2025, 18, 16155, 346, 20311, 24, 2279, 679, 0], [26618, 16155, 346, 33383, 0]]}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 113,
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+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/1659083 [00:00, ? examples/s]"
+ ],
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+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/520 [00:00, ? examples/s]"
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+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ "metadata": {}
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+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/2507 [00:00, ? examples/s]"
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+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 170,
+ "referenced_widgets": [
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+ "id": "7SNMAcfjLMkJ",
+ "outputId": "e51875c1-382a-41da-f2c9-f6e4b8fed8d6"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tf_model.h5: 0%| | 0.00/306M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "20fcebadbc2e49b09fa64ad30060f006"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "All model checkpoint layers were used when initializing TFMarianMTModel.\n",
+ "\n",
+ "All the layers of TFMarianMTModel were initialized from the model checkpoint at Helsinki-NLP/opus-mt-en-hi.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMarianMTModel for predictions without further training.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "generation_config.json: 0%| | 0.00/293 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6575afddbef14b4cb1245d3782037d77"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "batch_size = 16\n",
+ "learning_rate = 2e-5\n",
+ "weight_decay = 0.01\n",
+ "num_train_epochs = 1"
+ ],
+ "metadata": {
+ "id": "OPcFaPZfLStm"
+ },
+ "execution_count": 17,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
+ ],
+ "metadata": {
+ "id": "yjsI5OuOLWv5"
+ },
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "generation_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\", pad_to_multiple_of=128)"
+ ],
+ "metadata": {
+ "id": "s9upZ8cILZeH"
+ },
+ "execution_count": 19,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_dataset = model.prepare_tf_dataset(\n",
+ " tokenized_datasets[\"test\"],\n",
+ " batch_size=batch_size,\n",
+ " shuffle=True,\n",
+ " collate_fn=data_collator,\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "_uISyT0HLcqV"
+ },
+ "execution_count": 20,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "validation_dataset = model.prepare_tf_dataset(\n",
+ " tokenized_datasets[\"validation\"],\n",
+ " batch_size=batch_size,\n",
+ " shuffle=False,\n",
+ " collate_fn=data_collator,\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "dZE74QZCLfTM"
+ },
+ "execution_count": 21,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "generation_dataset = model.prepare_tf_dataset(\n",
+ " tokenized_datasets[\"validation\"],\n",
+ " batch_size=8,\n",
+ " shuffle=False,\n",
+ " collate_fn=generation_data_collator,\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "gkWAM1L5LgBm"
+ },
+ "execution_count": 22,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "optimizer = AdamWeightDecay(learning_rate=learning_rate, weight_decay_rate=weight_decay)\n",
+ "model.compile(optimizer=optimizer)"
+ ],
+ "metadata": {
+ "id": "PAXzh0AWLjYq"
+ },
+ "execution_count": 23,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.fit(train_dataset, validation_data=validation_dataset, epochs=1)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "f3pn7Bw1Lmlk",
+ "outputId": "1505eb0b-738d-4a32-def1-2573614ae872"
+ },
+ "execution_count": 24,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "156/156 [==============================] - 91s 340ms/step - loss: 3.7524 - val_loss: 3.9512\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "execution_count": 24
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.save_pretrained(\"tf_model/\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tMDD0OEtLo24",
+ "outputId": "cc7bb935-771a-443d-cb29-e7fee7743473"
+ },
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/transformers/configuration_utils.py:393: UserWarning: Some non-default generation parameters are set in the model config. These should go into either a) `model.generation_config` (as opposed to `model.config`); OR b) a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model).This warning will become an exception in the future.\n",
+ "Non-default generation parameters: {'max_length': 512, 'num_beams': 4, 'bad_words_ids': [[61949]]}\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#Model Testing\n",
+ "---"
+ ],
+ "metadata": {
+ "id": "vCvOgKlqLrH-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
+ "model = TFAutoModelForSeq2SeqLM.from_pretrained(\"tf_model/\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tnLiSvdWLwSo",
+ "outputId": "c67c26fa-d219-4e25-8b1a-180439200734"
+ },
+ "execution_count": 26,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
+ " warnings.warn(\"Recommended: pip install sacremoses.\")\n",
+ "All model checkpoint layers were used when initializing TFMarianMTModel.\n",
+ "\n",
+ "All the layers of TFMarianMTModel were initialized from the model checkpoint at tf_model/.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMarianMTModel for predictions without further training.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "input_text = \"Get ready for dinner !\"\n",
+ "\n",
+ "tokenized = tokenizer([input_text], return_tensors='np')\n",
+ "out = model.generate(**tokenized, max_length=128)\n",
+ "print(out)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t2nfPTQlLzr5",
+ "outputId": "8978a651-4b0e-4ecd-8961-98e4555f95f1"
+ },
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "tf.Tensor([[61949 810 6 1759 6 39 805 51 806 61 0]], shape=(1, 11), dtype=int32)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with tokenizer.as_target_tokenizer():\n",
+ " print(tokenizer.decode(out[0], skip_special_tokens=True))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "caPTWhnnL2Tq",
+ "outputId": "d1a7c752-64d0-4c11-91dd-8a0584c03718"
+ },
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "रात के खाने के लिए तैयार हो जाओ!\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "ZbC9ddDmT4PM"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file