From f8ce62f72cf243c0059b592aa7fbbc452c6b7aac Mon Sep 17 00:00:00 2001 From: Rishik Date: Fri, 18 Oct 2024 15:30:30 +0530 Subject: [PATCH 1/2] Added a new Project idea in 1-Beginner as todo_list_project --- Projects/1-Beginner/todo_list_project.md | 49 ++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 Projects/1-Beginner/todo_list_project.md diff --git a/Projects/1-Beginner/todo_list_project.md b/Projects/1-Beginner/todo_list_project.md new file mode 100644 index 00000000..8e2c8b71 --- /dev/null +++ b/Projects/1-Beginner/todo_list_project.md @@ -0,0 +1,49 @@ + +# To-Do List Project File + +## Project Title: To-Do List Application + +### Short Description +A basic to-do list application where users can add, remove, and check off tasks. + +### Tier: 1-Beginner + +### Project Overview +The To-Do List application allows users to manage their tasks efficiently. Users can add new tasks, remove completed tasks, and mark tasks as completed. The application aims to provide a simple yet effective way for users to organize their daily activities. + +### User Stories +- View Tasks: User can see a list of all tasks currently on their to-do list. +- Add Tasks: User can add a new task by entering text into an input field and clicking an 'Add' button. +- Remove Tasks: User can remove a task from the list by clicking a 'Delete' button next to the task. +- Mark Tasks as Completed: User can click a checkbox next to a task to mark it as completed, visually indicating the task is done. +- Clear All Tasks: User can clear all tasks with a 'Clear All' button. +- Save Tasks: User’s tasks are saved in local storage, so they remain available even after the page is refreshed. + +### Features +- Input Field: A text input field for users to enter their tasks. +- Add Button: A button that adds the entered task to the list. +- Task List: An unordered list that displays all tasks. +- Delete Button: A button next to each task to remove it from the list. +- Checkbox: A black checkbox to mark tasks as completed. +- Clear All Button: A button to remove all tasks from the list. +- Local Storage: Use local storage to save tasks, allowing persistence between sessions. + +### Constraints +- Tasks should not exceed 100 characters. +- Ensure the input field does not accept empty tasks. +- Use clear, user-friendly messages for any errors (e.g., when attempting to add an empty task). + +### Bonus Features +- Edit Tasks: Allow users to edit existing tasks by clicking on them. +- Due Dates: Users can set due dates for tasks and sort tasks based on these dates. +- Priority Levels: Users can assign priority levels (e.g., low, medium, high) to tasks. + +### Useful Links and Resources +- [MDN Web Docs - Web Storage API](https://developer.mozilla.org/en-US/docs/Web/API/Web_Storage_API) +- [JavaScript Array Methods](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array) +- [CSS Flexbox Guide](https://css-tricks.com/snippets/css/a-guide-to-flexbox/) + +### Example Projects +- Simple To-Do List +- Task Manager App +- JavaScript To-Do List From 5ade761bd6d330bdf185a6fd56f927654cc08e07 Mon Sep 17 00:00:00 2001 From: Rishik <86944435+RISHIK92@users.noreply.github.com> Date: Tue, 18 Nov 2025 01:53:59 +0530 Subject: [PATCH 2/2] Created using Colab --- Untitled0.ipynb | 4531 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 4531 insertions(+) create mode 100644 Untitled0.ipynb diff --git a/Untitled0.ipynb b/Untitled0.ipynb new file mode 100644 index 00000000..cd3a91e9 --- /dev/null +++ b/Untitled0.ipynb @@ -0,0 +1,4531 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true, + "gpuType": "T4", + "mount_file_id": "1CgGSvTjK45v-48hmy09dyFKTpV0zgbHD", + "authorship_tag": "ABX9TyMd9LqAc6vaEQDNZfg51W3P", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + 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(2025.10.5)\n" + ] + } + ], + "source": [ + "!pip install transformers datasets evaluate torch scikit-learn pandas\n", + "!pip install -U transformers\n", + "\n", + "from datasets import load_dataset\n", + "import evaluate\n", + "metric = evaluate.load(\"accuracy\")\n", + "\n", + "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "source": [ + "# Install dependencies\n", + "import sys\n", + "!{sys.executable} -m pip install -q evaluate\n", + "!{sys.executable} -m pip install -q transformers\n", + "\n", + "from datasets import load_dataset\n", + "import evaluate\n", + "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n", + "import numpy as np\n", + "import torch\n", + "\n", + "# ===============================\n", + "# 1. Load Dataset\n", + "# ===============================\n", + "dataset = load_dataset(\n", + " \"csv\",\n", + " data_files=\"/content/sample_data/cleaned_liar_dataset.csv\"\n", + ")\n", + "\n", + "dataset = dataset[\"train\"].train_test_split(test_size=0.2, seed=42)\n", + "\n", + "# ===============================\n", + "# 2. Build rich input text\n", + "# ===============================\n", + "def build_text(example):\n", + " parts = [\n", + " example.get(\"statement\", \"\"),\n", + " example.get(\"subject\", \"\"),\n", + " example.get(\"speaker\", \"\"),\n", + " example.get(\"party_affiliation\", \"\"),\n", + " example.get(\"context\", \"\")\n", + " ]\n", + " example[\"text\"] = \" [SEP] \".join([p for p in parts if p])\n", + " return example\n", + "\n", + "dataset = dataset.map(build_text)\n", + "\n", + "# ===============================\n", + "# 3. Encode labels\n", + "# ===============================\n", + "unique_labels = sorted(dataset[\"train\"].unique(\"label\"))\n", + "label2id = {label: i for i, label in enumerate(unique_labels)}\n", + "id2label = {i: label for label, i in label2id.items()}\n", + "\n", + "def encode_labels(example):\n", + " example[\"label\"] = label2id[example[\"label\"]]\n", + " return example\n", + "\n", + "dataset = dataset.map(encode_labels)\n", + "\n", + "print(\"Label mapping:\", label2id)\n", + "\n", + "# ===============================\n", + "# 4. Tokenize\n", + "# ===============================\n", + "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/deberta-v3-base\")\n", + "\n", + "def tokenize_function(examples):\n", + " return tokenizer(\n", + " examples[\"text\"],\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " max_length=384 # DeBERTa benefits from longer context\n", + " )\n", + "\n", + "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n", + "\n", + "# ===============================\n", + "# 5. Load DeBERTa-v3-base\n", + "# ===============================\n", + "model = AutoModelForSequenceClassification.from_pretrained(\n", + " \"microsoft/deberta-v3-base\",\n", + " num_labels=len(unique_labels),\n", + " id2label=id2label,\n", + " label2id=label2id\n", + ")\n", + "\n", + "# ===============================\n", + "# 6. Training Arguments (older HF compatible)\n", + "# ===============================\n", + "training_args = TrainingArguments(\n", + " output_dir=\"./results\",\n", + " do_train=True,\n", + " do_eval=True,\n", + " per_device_train_batch_size=8, # DeBERTa is heavier than DistilBERT\n", + " per_device_eval_batch_size=8,\n", + " learning_rate=2e-5,\n", + " num_train_epochs=5, # Better convergence\n", + " logging_steps=50,\n", + " fp16=torch.cuda.is_available(), # Use fp16 if GPU exists\n", + " save_total_limit=1 # Prevent disk overflow\n", + ")\n", + "\n", + "# ===============================\n", + "# 7. Metrics\n", + "# ===============================\n", + "metric = evaluate.load(\"f1\")\n", + "\n", + "def compute_metrics(eval_pred):\n", + " logits, labels = eval_pred\n", + " preds = np.argmax(logits, axis=-1)\n", + " return metric.compute(predictions=preds, references=labels, average=\"weighted\")\n", + "\n", + "# ===============================\n", + "# 8. Trainer\n", + "# ===============================\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=tokenized_datasets[\"train\"],\n", + " eval_dataset=tokenized_datasets[\"test\"],\n", + " compute_metrics=compute_metrics,\n", + " tokenizer=tokenizer\n", + ")\n", + "\n", + "# ===============================\n", + "# 9. Train\n", + "# ===============================\n", + "print(\"Starting DeBERTa training...\")\n", + "trainer.train()\n", + "\n", + "# ===============================\n", + "# 10. Evaluate\n", + "# ===============================\n", + "print(\"\\nEvaluating model...\")\n", + "results = trainer.evaluate()\n", + "print(\"\\nFinal Results:\")\n", + "print(results)\n", + "\n", + "# ===============================\n", + "# 11. Save model\n", + "# ===============================\n", + "trainer.save_model(\"./deberta_liar_model\")\n", + "tokenizer.save_pretrained(\"./deberta_liar_model\")\n", + "\n", + "print(\"\\nModel saved to ./deberta_liar_model\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "9fab6203abf947d6aaefdef206de166d", + "4bec02b298c84d369aee499a86810ace", + "a8c13c17a05c457abf3e9bd9961e9031", + "fb9a215777b64ce98206253c7417d8e9", + "d7d758e48d4a4be3bb55b5fab250d7af", + "a342f8b36d954e4bbb24285881425d13", + "affae9d1640a4cf3aee747ef3595412c", + "784ff9bf595241c29066857e4b67df73", + "67277bed3665416288b27ec307b3a614", + "4d0600b3349c450c9b3c80fa58f5e303", + "3cd5bab1d5604c52ad241fd373cb0574", + "64289697b6064c1c8098c07b52247345", + "20603652f27946468e3d49ce014ceb5c", + "a9e3ec2856a74c00b160b830196f34ec", + "b75198600f6e487d9673508cad0da93d", + "713e155c45fa41578a93f2b41994ad6b", + "fa2579a44512448aa4e53fed733f35a8", + 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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Evaluating model...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

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