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BartTokenizer, Trainer, TrainingArguments\n", "from transformers import DataCollatorForSeq2Seq\n", "\n", "# Load dataset (replace with your actual dataset path)\n", "dataset = load_dataset(\"sudeshna84/Hind-Beng-5k\")\n", "\n", "# Load BART tokenizer and model for translation\n", "model_name = \"facebook/bart-large\" # You can use a smaller variant like bart-base as well\n", "tokenizer = BartTokenizer.from_pretrained(model_name)\n", "model = BartForConditionalGeneration.from_pretrained(model_name)\n", "\n", "# Preprocessing function for translation\n", "def preprocess_function(examples):\n", " inputs = [f\"translate Hindi to Bengali: {text}\" for text in examples['hi']] # Hindi text\n", " targets = examples['bn'] # Bengali text (target)\n", " model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding=\"max_length\")\n", " with tokenizer.as_target_tokenizer():\n", " labels = tokenizer(targets, max_length=128, truncation=True, padding=\"max_length\")\n", " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", " return model_inputs\n", "\n", "# Apply preprocessing\n", "train_dataset = dataset[\"train\"].map(preprocess_function, batched=True)\n", "eval_dataset = dataset[\"test\"].map(preprocess_function, batched=True)\n", "\n", "# Set up data collator\n", "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n", "\n", "# Set up training arguments\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\", # output directory for model checkpoints\n", " evaluation_strategy=\"epoch\", # evaluation at each epoch\n", " learning_rate=2e-5, # initial learning rate\n", " per_device_train_batch_size=8, # batch size per device during training\n", " per_device_eval_batch_size=8, # batch size per device during evaluation\n", " num_train_epochs=3, # number of training epochs\n", " weight_decay=0.01, # strength of weight decay for regularization\n", " logging_dir=\"./logs\", # directory for storing logs\n", " logging_steps=10, # log every 10 steps\n", " save_steps=500, # save checkpoints every 500 steps\n", " eval_accumulation_steps=2, # accumulate gradients before evaluation\n", ")\n", "\n", "# Initialize Trainer\n", "trainer = Trainer(\n", " model=model, # the model to train\n", " args=training_args, # training arguments\n", " train_dataset=train_dataset, # training dataset\n", " eval_dataset=eval_dataset, # evaluation dataset\n", " data_collator=data_collator, # data collator to pad inputs\n", " tokenizer=tokenizer, # tokenizer\n", ")\n", "\n", "# Start training\n", "trainer.train()\n", "\n", "# Save the fine-tuned model\n", "model.save_pretrained(\"./fine_tuned_bart_model\")\n", "tokenizer.save_pretrained(\"./fine_tuned_bart_model\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "b5ef33b58aa94836ad286f8b75b6feab", "06853a09fcb943faa89748f307905e25", "74401b0b6b4f4931989903646426ff5b", "723d5d0f6fc94ad6b2f07e93598b43d8", 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to access public models or datasets.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b5ef33b58aa94836ad286f8b75b6feab", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/425 [00:00:48: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n" ] }, { "data": { "application/javascript": [ "\n", " window._wandbApiKey = new Promise((resolve, reject) => {\n", " function loadScript(url) {\n", " return new Promise(function(resolve, reject) {\n", " let newScript = document.createElement(\"script\");\n", " newScript.onerror = reject;\n", " newScript.onload = resolve;\n", " document.body.appendChild(newScript);\n", " newScript.src = url;\n", " });\n", " }\n", " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", " const iframe = document.createElement('iframe')\n", " iframe.style.cssText = \"width:0;height:0;border:none\"\n", " document.body.appendChild(iframe)\n", " const handshake = new Postmate({\n", " container: iframe,\n", " url: 'https://wandb.ai/authorize'\n", " });\n", " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", " handshake.then(function(child) {\n", " child.on('authorize', data => {\n", " clearTimeout(timeout)\n", " resolve(data)\n", " });\n", " });\n", " })\n", " });\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", "wandb: Paste an API key from your profile and hit enter:\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33marunapriyad24\u001b[0m (\u001b[33marunapriyad24-woxsen-university\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. 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10.8405000.804304

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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "('./fine_tuned_bart_model/tokenizer_config.json',\n", " './fine_tuned_bart_model/special_tokens_map.json',\n", " './fine_tuned_bart_model/vocab.json',\n", " './fine_tuned_bart_model/merges.txt',\n", " './fine_tuned_bart_model/added_tokens.json')" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OyEYfLlWBI3L", "outputId": "576dfbbd-e59c-4758-fcbe-6729e95f3ad1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Hindi: फिर एक बार, मन की बातें करने के लिए, आपके बीच आने का मुझे अवसर मिला है।\n", "Bengali: একবার, মনের প্রতিবীচ কোথা লইয়া , আপনি সুরূষ এ অসব হয়।\n", "--------------------------------------------------\n", "Hindi: चिल्लाकर पुकारा “अजी हो क्या!\n", "Bengali: চিল্লাকর পুকেরা “অজী হইয়া!\n", "--------------------------------------------------\n" ] } ], "source": [ "from transformers import BartForConditionalGeneration, BartTokenizer\n", "\n", "# Load the fine-tuned model and tokenizer\n", "model = BartForConditionalGeneration.from_pretrained(\"./fine_tuned_bart_model\")\n", "tokenizer = BartTokenizer.from_pretrained(\"./fine_tuned_bart_model\")\n", "\n", "# Function to translate Hindi to Bengali using the fine-tuned model\n", "def translate_hindi_to_bengali(hindi_text):\n", " # Prepare the input prompt for BART\n", " input_text = f\"translate Hindi to Bengali: {hindi_text}\"\n", "\n", " # Tokenize the input\n", " inputs = tokenizer(input_text, return_tensors=\"pt\", max_length=128, truncation=True, padding=\"max_length\")\n", "\n", " # Generate translation\n", " with torch.no_grad():\n", " outputs = model.generate(**inputs, max_length=256)\n", "\n", "\n", " # Decode the generated tokens to Bengali text\n", " translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", " return translated_text\n", "\n", "# Example sentences to translate\n", "hindi_sentences = [\n", " \"फिर एक बार, मन की बातें करने के लिए, आपके बीच आने का मुझे अवसर मिला है।\",\n", " \"चिल्लाकर पुकारा “अजी हो क्या!\",\n", "\n", "]\n", "\n", "# Translate the example sentences\n", "for hindi_sentence in hindi_sentences:\n", " bengali_translation = translate_hindi_to_bengali(hindi_sentence)\n", " print(f\"Hindi: {hindi_sentence}\")\n", " print(f\"Bengali: {bengali_translation}\")\n", " print(\"-\" * 50)\n" ] }, { "cell_type": "code", "source": [ "torch.save(model.state_dict(), 'model.pth') # Save model weights\n" ], "metadata": { "id": "7-iafRpLBKGY" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "files.download('model.pth') # Replace 'model.h5' with your model's filename\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "AuZZXcJwMF6H", "outputId": "6355b073-3f25-4486-d4a7-80b148eb06a8" }, "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_124725d8-4f9b-4484-90b8-a0b54f2efa76\", \"model.pth\", 1625518762)" ] }, "metadata": {} } ] } ] }