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"cells": [
{
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"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/RISHIK92/app-ideas-ap/blob/master/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.1)\n",
"Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n",
"Requirement already satisfied: evaluate in /usr/local/lib/python3.12/dist-packages (0.4.6)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.8.0+cu126)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.36.0)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n",
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"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2024.11.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers) (2.32.4)\n",
"Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.1)\n",
"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.6.2)\n",
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"Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (25.4.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.8.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.7.0)\n",
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.4.1)\n",
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.22.0)\n",
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"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2024.11.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers) (2.32.4)\n",
"Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.1)\n",
"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.6.2)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (2025.3.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (4.15.0)\n",
"Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (1.2.0)\n",
"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.4.4)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.11)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (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,
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{
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"text": [
"Label mapping: {'barely-true': 0, 'false': 1, 'half-true': 2, 'mostly-true': 3, 'pants-fire': 4, 'true': 5}\n"
]
},
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{
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"text": [
"/usr/local/lib/python3.12/dist-packages/transformers/convert_slow_tokenizer.py:564: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
" warnings.warn(\n"
]
},
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{
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"text": [
"Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-v3-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
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},
{
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"name": "stderr",
"text": [
"/tmp/ipython-input-1837380874.py:107: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
" trainer = Trainer(\n",
"The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 2, 'bos_token_id': 1}.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Starting DeBERTa training...\n"
]
},
{
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"data": {
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],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='635' max='635' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [635/635 06:52, Epoch 5/5]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.777500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>1.748700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>1.720100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>1.755200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>1.725200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>1.678600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>1.658500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>1.625700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>1.545900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>1.528200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>1.410400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>1.387500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Evaluating model...\n"
]
},
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],
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"\n",
" <div>\n",
" \n",
" <progress value='32' max='32' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [32/32 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Final Results:\n",
"{'eval_loss': 1.8004469871520996, 'eval_f1': 0.2083629516058613, 'eval_runtime': 3.6035, 'eval_samples_per_second': 70.487, 'eval_steps_per_second': 8.88, 'epoch': 5.0}\n",
"\n",
"Model saved to ./deberta_liar_model\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"dataset['train'].column_names\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "A4_kclPQ1Yaq",
"outputId": "02dc9b2f-239c-42b8-dfdc-31723616f774"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['id',\n",
" 'label',\n",
" 'statement',\n",
" 'subject',\n",
" 'speaker',\n",
" 'job_title',\n",
" 'state_info',\n",
" 'party_affiliation',\n",
" 'barely_true_counts',\n",
" 'false_counts',\n",
" 'half_true_counts',\n",
" 'mostly_true_counts',\n",
" 'pants_on_fire_counts',\n",
" 'context',\n",
" 'target']"
]
},
"metadata": {},
"execution_count": 8
}
]
}
]
}