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pd\n", "\n", "# Emotion dataset\n", "em_ds = load_dataset(\"dair-ai/emotion\")\n", "em_df = em_ds[\"train\"].to_pandas()[[\"text\", \"label\"]]\n", "em_df.to_csv(\"emotion_dataset.csv\", index=False)\n", "\n", "# Simple working financial dataset\n", "fin_ds = load_dataset(\"zeroshot/twitter-financial-news-sentiment\")\n", "fin_sample = fin_ds[\"train\"].to_pandas()\n", "\n", "# This dataset has 'text' and 'label' columns\n", "if 'text' in fin_sample.columns and 'label' in fin_sample.columns:\n", " fin_df = fin_sample[[\"text\", \"label\"]]\n", "else:\n", " # Backup: create from first two columns\n", " fin_df = fin_sample.iloc[:, [0, 1]].copy()\n", " fin_df.columns = ['text', 'label']\n", "\n", "fin_df.to_csv(\"financial_dataset.csv\", index=False)\n", "\n", "print(\"Emotion samples:\", len(em_df))\n", "print(\"Financial samples:\", len(fin_df))\n", "print(\"Financial labels:\", fin_df['label'].unique())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 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0%| | 0/9543 [00:00\n", "
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textlabel
0i didnt feel humiliated0
1i can go from feeling so hopeless to so damned...0
2im grabbing a minute to post i feel greedy wrong3
3i am ever feeling nostalgic about the fireplac...2
4i am feeling grouchy3
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "em_df", "summary": "{\n \"name\": \"em_df\",\n \"rows\": 16000,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 15969,\n \"samples\": [\n \"i feel rather imbicilic or at least complacent\",\n \"i was in the bathroom i had sat down to pee it was to make me feel submissive again per instructions\",\n \"i am thrilled with the way my skin and hair feel if you are like me you are skeptical\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 5,\n \"num_unique_values\": 6,\n \"samples\": [\n 0,\n 3,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "import pandas as pd, numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder\n", "from datasets import Dataset\n", "from transformers import (AutoTokenizer, AutoModelForSequenceClassification,\n", " Trainer, TrainingArguments)\n", "from sklearn.metrics import accuracy_score, f1_score\n", "import joblib, torch\n", "\n", "# ---------- Load & encode ----------\n", "em_df = pd.read_csv(\"emotion_dataset.csv\")\n", "le_em = LabelEncoder()\n", "em_df[\"labels\"] = le_em.fit_transform(em_df[\"label\"])\n", "\n", "train_df, val_df = train_test_split(\n", " em_df, test_size=0.2, random_state=42, stratify=em_df[\"labels\"]\n", ")\n", "\n", "train_ds = Dataset.from_pandas(train_df[[\"text\", \"labels\"]])\n", "val_ds = Dataset.from_pandas(val_df[[\"text\", \"labels\"]])\n", "\n", "# ---------- Tokenize ----------\n", "tok_em = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n", "def tok(batch):\n", " return tok_em(batch[\"text\"], truncation=True, padding=\"max_length\", max_length=128)\n", "\n", "train_ds = train_ds.map(tok, batched=True)\n", "val_ds = val_ds.map(tok, batched=True)\n", "\n", "# ---------- Create label mappings ----------\n", "classes = le_em.classes_\n", "id2label = {i: str(label) for i, label in enumerate(classes)}\n", "label2id = {str(label): i for i, label in enumerate(classes)}\n", "\n", "print(\"Emotion labels:\", list(classes))\n", "\n", "# ---------- Model ----------\n", "model_em = AutoModelForSequenceClassification.from_pretrained(\n", " \"distilbert-base-uncased\",\n", " num_labels=len(classes),\n", " id2label=id2label,\n", " label2id=label2id\n", ")\n", "\n", "# ---------- Metrics ----------\n", "def compute_metrics(eval_pred):\n", " predictions, labels = eval_pred\n", " preds = np.argmax(predictions, axis=1)\n", " return {\n", " \"accuracy\": accuracy_score(labels, preds),\n", " \"f1\": f1_score(labels, preds, average=\"weighted\")\n", " }\n", "\n", "# ---------- Training arguments (FIXED parameter names) ----------\n", "training_args = TrainingArguments(\n", " output_dir=\"emotion_model\",\n", " num_train_epochs=3,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True,\n", " fp16=torch.cuda.is_available(),\n", " logging_steps=50,\n", " report_to=[] # This disables wandb completely\n", ")\n", "\n", "# ---------- Trainer ----------\n", "trainer = Trainer(\n", " model=model_em,\n", " args=training_args,\n", " train_dataset=train_ds,\n", " eval_dataset=val_ds,\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "# ---------- Train ----------\n", "print(\"🏋️ Starting emotion model training...\")\n", "trainer.train()\n", "\n", "# ---------- Save ----------\n", "trainer.save_model(\"emotion_model\")\n", "tok_em.save_pretrained(\"emotion_model\")\n", "joblib.dump(le_em, \"emotion_model/label_encoder.pkl\")\n", "\n", "print(\"✅ Emotion model training completed!\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 340, "referenced_widgets": [ "138fb603d2e5483797e6c269c1140ac0", "7491b4c91fb84e59b7fae23d7f4c9160", "e06808d0efb84bb9ad129a4d7b41c675", "9b31293550e543869198c50d25847b2d", "88705415405546adb1cf28581f960c99", "abeed6f3a7b84e358b45f76e3c4ac605", "4d1b0144c76d4af4a54f1f445acd5e7e", "3d9642a0054e41f786c7aa2007a44253", "39338e48270e43a3a86dbdd7b36bfb38", "c557f4f2d21e4a47baedd4754f98fb86", "db65f4c18f9c4df8894fb98899dc9d54", "be040befc3bd48c083f43256c2bb690b", "3420594340384073a6bb8d2d8572f8ec", "29594aead2d0482d9e9ae309f621e539", "d58907cc42da463fa6a4cad3e2761e67", "556db45d653347c8aa49db43a3028cb5", "c6511c428831427084451004d4f8190f", "99ce8736638b4daeadbf463eca56908c", "cee75c8fa8c84fd98e9d7dd8706b42b6", "8717bc30c3a5428db0a583b43a8e691c", "669c561d1bb54e9b9227f5538917ba37", "bfc52ad7e7fc4e0495bfec47203247d0" ] }, "id": "OE3GH_2bdMaX", "outputId": "1445981b-56b1-4fe8-f7bf-15069838c7cf" }, "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/12800 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [2400/2400 03:59, Epoch 3/3]\n", "
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EpochTraining LossValidation LossAccuracyF1
10.1749000.2065380.9306250.931850
20.1487000.1693850.9340630.932175
30.0781000.1798550.9356250.935530

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Emotion model training completed!\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd, numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder\n", "from datasets import Dataset\n", "from transformers import (AutoTokenizer, AutoModelForSequenceClassification,\n", " Trainer, TrainingArguments)\n", "from sklearn.metrics import accuracy_score, f1_score\n", "import joblib, torch\n", "\n", "# ---------- Load & encode ----------\n", "fin_df = pd.read_csv(\"financial_dataset.csv\")\n", "le_fin = LabelEncoder()\n", "fin_df[\"labels\"] = le_fin.fit_transform(fin_df[\"label\"])\n", "\n", "train_df, val_df = train_test_split(\n", " fin_df, test_size=0.2, random_state=42, stratify=fin_df[\"labels\"]\n", ")\n", "\n", "train_ds = Dataset.from_pandas(train_df[[\"text\", \"labels\"]])\n", "val_ds = Dataset.from_pandas(val_df[[\"text\", \"labels\"]])\n", "\n", "# ---------- Tokenize ----------\n", "tok_fin = AutoTokenizer.from_pretrained(\"ProsusAI/finbert\")\n", "def tok(batch):\n", " return tok_fin(batch[\"text\"], truncation=True, padding=\"max_length\", max_length=128)\n", "\n", "train_ds = train_ds.map(tok, batched=True)\n", "val_ds = val_ds.map(tok, batched=True)\n", "\n", "# ---------- Create label mappings ----------\n", "classes = le_fin.classes_\n", "id2label = {i: str(label) for i, label in enumerate(classes)}\n", "label2id = {str(label): i for i, label in enumerate(classes)}\n", "\n", "print(\"Financial labels:\", list(classes))\n", "\n", "# ---------- Model ----------\n", "model_fin = AutoModelForSequenceClassification.from_pretrained(\n", " \"ProsusAI/finbert\",\n", " num_labels=len(classes),\n", " id2label=id2label,\n", " label2id=label2id\n", ")\n", "\n", "# ---------- Metrics ----------\n", "def compute_metrics(eval_pred):\n", " predictions, labels = eval_pred\n", " preds = np.argmax(predictions, axis=1)\n", " return {\n", " \"accuracy\": accuracy_score(labels, preds),\n", " \"f1\": f1_score(labels, preds, average=\"weighted\")\n", " }\n", "\n", "# ---------- Training arguments (FIXED parameter names) ----------\n", "training_args = TrainingArguments(\n", " output_dir=\"finbert_model\",\n", " num_train_epochs=3,\n", " per_device_train_batch_size=8,\n", " per_device_eval_batch_size=8,\n", " eval_strategy=\"epoch\", # Changed from 'evaluation_strategy'\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True,\n", " fp16=torch.cuda.is_available(),\n", " learning_rate=2e-5,\n", " logging_steps=25,\n", " report_to=[] # This disables wandb completely\n", ")\n", "\n", "# ---------- Trainer ----------\n", "trainer = Trainer(\n", " model=model_fin,\n", " args=training_args,\n", " train_dataset=train_ds,\n", " eval_dataset=val_ds,\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "# ---------- Train ----------\n", "print(\"🏋️ Starting financial model training...\")\n", "trainer.train()\n", "\n", "# ---------- Save ----------\n", "trainer.save_model(\"finbert_model\")\n", "tok_fin.save_pretrained(\"finbert_model\")\n", "joblib.dump(le_fin, \"finbert_model/label_encoder.pkl\")\n", "\n", "print(\" Financial model training completed!\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 477, "referenced_widgets": [ "318092d3fcba49d5916ba4f72056ebbe", "8d555699705448c99c90d1556de7ab17", "831a6c59189f48bc85a21951570c70d6", "b639847adf08414186c4bbba0fa0deb9", "0a660a4a151a4f47ac8da1e3317573f4", "a2e331619d734984b84f8d134333ee90", "64dc7ccbecb04a8db48fb66fce4e5cd7", "7c65abdc3c0f46808490ccba671f78a8", "7f0fc330acc34c23ac929f3d1762551d", "8b475289f1dd4cbf9cf467494aed0052", "e42754a05a3641a3bd1ad5deec1404fe", "865c1ba07a734fb3ad662068874741b5", "9aad819c1b2f4731bb2bec77e50ef26f", "e7cbea6b35c5492fadfe8acd15b4e65b", "bd4c94f9961f465196adafa03a5053cb", "73067e0ec3494dffab1f50346d9c16fa", "0394a33d108341a986919d89d6418641", "da6f5420eaf545bfa97650ef51ad75ee", "1a2bcb5beb5e4fbb9866baa70a6fc8c0", "584e63b68f94442581c8fd409bece1fd", "5c3d0791c5534f59b90fdf1840135efd", "27466c209b2c45138970f56a6e561151", "ea783b80443d4304bb4e78cd3838b5df", "938ec750a25347e8a711707c2780d0e1", "34fa9615c0b74f88b5f7c5cb20a0cfb5", "54f4d0c153954953ac0b4e8c049c93e3", "e94a3ec3f9a54013a6abc91a70c2941d", "e31c6c312f474cae80ab4595175c61f6", "bbbbc81278a447a481650b22b928e793", "e6eb636d16774dc5a891f668df5bba53", "1a11e8f000b84b69b9f33776e1d5293f", "e155a9f8511b4483aeab7c876d2bdd21", "7a8c467cfeb1438187dc0c6703d6d87f", "386c0f011d0047eea31bed699d6507b3", "d3f55218ebe3460395a96b36d8e11d66", "9a1555f585ff4eb0b1e2d090e774e89a", "7c5948c380804d619e4f12b78b3cc5cd", "12eff520fdba4316bdff88f5c9e6d906", "c2eea07ae40b435fb1bd48899cfa2774", "55977fda50ff46f2b2b64daf69292285", "73ecf81d3860445686c0961942cca91f", "44b076a162c44faca66296ae6c8fd513", "b85385b200044ae49f41d128b39a8408", "eb08fd82059e46a9beb7fbffee2fb95d", "42085813787f4512adc873510ba79411", "8f00a9bfb41244029834ac9eef025ce3", "908ce150b550440485e31115bffa175a", "a680db208df94f6d8b686653c24fc096", "e576d4520cdd4c00aba5cd1458436d30", "4088e27ab1ee43d4b9b7e1d34976c173", "e66260f4f813413c8949ada859f34305", "954f6f9e1a8542d6a9c020e3271c1fc2", "d832aca2d4dc467d84ad92d32bae6d6b", "5ffee6271472449c8284e14f4da286b9", "01bd887efb434ee399066042c80ef845", "db45d2660f3a49068e7464b59329984e", "b3f51af1ae8343bfbd3b472ed59f8fa0", "23ae8b8e35ac4d1396065d23867f4edf", "49027c46769949ceb29ed2f5ea281f16", "3365c2d57b2344a796bedbabd8b4e0ac", "d87c542b600043b5b1c63c0aeb450c70", "93392fe01698467da9328e7513d73b13", "ebc98b8a9f5349f5ab59cac0ccfa9798", "cccfd8c8a53444e48be017ed947271c3", "ba6487f079664559b29c69d263ef2bf6", "bf00c7fd14ee4b82a11ffdffc3c6163f", "3eb06583432c48e3b98615651396648d", "03a9560dd8ec4f699a3f756ce4433f86", "589dfb291bca4f5da0a9e66f5d15fbea", "9c1d3c6e5d7b4a02b86e7f889ebf3ff3", "749b19b3091d4c3183a4bdbe3b4731bf", "7cb7481df24d4cceb9e2dc493f221e34", "0628f7e5104e47dc8a84d7d94f6c3343", "d2a2f895b7334264af31d2c664193eaf", "5ddedf47718d4c0b9b0496dc7cf28ae1", "2b62a31bc5b44eb5822b6f1b5da95f1a", "99459bc9d5ad4483a6236237856f44ea", "1a481a0af5514182aa773208cbcad6ef", "c07f55bd23f24bdfa0b6e2ccc7688c9e", "9a90943d2de34e76bcf70d259595bf2a", "ab927495036e4ea5873fc746ce428b25", "95c58ec6c05247bda566b6a9b1174aa0", "86c3ca61f2ab4ca682544403afd3adce", "525f2e88787f4e7cace0b482a33f2592", "c60b527b43d4479ea49c73f0ae3ad3ff", "45a2a35212c34dc6980237dfdb0567d4", "2c2e04c480d64aca97183f0494546cc7", "aaf2ff160e80447abaf24d2a2f30b5e0" ] }, "id": "6J_ardBfdNUo", "outputId": "77a02a93-a748-49ef-86fc-d8ad9763ac0d" }, "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/252 [00:00" ], "text/html": [ "\n", "

\n", " \n", " \n", " [2865/2865 07:22, Epoch 3/3]\n", "
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EpochTraining LossValidation LossAccuracyF1
10.4069000.3973130.8690410.866500
20.2418000.4636140.8727080.872882
30.1826000.5452300.8779470.877347

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Financial model training completed!\n" ] } ] }, { "cell_type": "code", "source": [ "# Test both models to make sure they work\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", "import torch\n", "import joblib\n", "\n", "print(\"🧪 Testing trained models...\")\n", "\n", "# Test emotion model\n", "print(\"\\n🎭 Testing emotion model:\")\n", "em_tokenizer = AutoTokenizer.from_pretrained(\"emotion_model\")\n", "em_model = AutoModelForSequenceClassification.from_pretrained(\"emotion_model\")\n", "\n", "# Load the label encoder we saved during training\n", "le_em = joblib.load(\"emotion_model/label_encoder.pkl\")\n", "\n", "test_text = \"I'm so excited about this project!\"\n", "inputs = em_tokenizer(test_text, return_tensors=\"pt\", truncation=True, padding=True)\n", "with torch.no_grad():\n", " outputs = em_model(**inputs)\n", " prediction = torch.argmax(outputs.logits, dim=1).item()\n", "\n", " # Use the label encoder to get the actual emotion name\n", " predicted_label = le_em.classes_[prediction]\n", "\n", "print(f\"Text: '{test_text}'\")\n", "print(f\"Predicted emotion: {predicted_label}\")\n", "print(f\"Prediction confidence: {torch.softmax(outputs.logits, dim=1).max().item():.3f}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WhdVXhtnlTwh", "outputId": "54c8f4e5-8ea1-4a45-b6ea-cc7a3b0b0412" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🧪 Testing trained models...\n", "\n", "🎭 Testing emotion model:\n", "Text: 'I'm so excited about this project!'\n", "Predicted emotion: 1\n", "Prediction confidence: 0.994\n" ] } ] }, { "cell_type": "code", "source": [ "import shutil\n", "import zipfile\n", "import os\n", "from google.colab import files\n", "\n", "def create_model_zip(model_dir, zip_filename):\n", " \"\"\"Create a zip file from model directory\"\"\"\n", " print(f\" Creating {zip_filename}...\")\n", "\n", " with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zipf:\n", " for root, dirs, files_list in os.walk(model_dir):\n", " for file in files_list:\n", " file_path = os.path.join(root, file)\n", " # Add file to zip with relative path\n", " arcname = os.path.relpath(file_path, model_dir)\n", " zipf.write(file_path, arcname)\n", "\n", " # Show what's in the zip\n", " with zipfile.ZipFile(zip_filename, 'r') as zipf:\n", " print(f\"📋 Files in {zip_filename}:\")\n", " for file in zipf.namelist():\n", " print(f\" 📄 {file}\")\n", "\n", " return zip_filename\n", "\n", "# Create zip files for both models\n", "emotion_zip = create_model_zip(\"emotion_model\", \"emotion_model.zip\")\n", "finbert_zip = create_model_zip(\"finbert_model\", \"finbert_model.zip\")\n", "\n", "print(f\"\\n Model sizes:\")\n", "print(f\" Emotion model: {os.path.getsize(emotion_zip) / (1024*1024):.1f} MB\")\n", "print(f\" FinBERT model: {os.path.getsize(finbert_zip) / (1024*1024):.1f} MB\")\n", "\n", "print(f\"\\n Models packaged successfully!\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sV6I2WaFmB1I", "outputId": "0b0d1fa1-1044-4a64-ca78-cf2e0fb0a8fa" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Creating emotion_model.zip...\n", "📋 Files in emotion_model.zip:\n", " 📄 vocab.txt\n", " 📄 special_tokens_map.json\n", " 📄 tokenizer.json\n", " 📄 config.json\n", " 📄 label_encoder.pkl\n", " 📄 training_args.bin\n", " 📄 model.safetensors\n", " 📄 tokenizer_config.json\n", " 📄 checkpoint-2400/config.json\n", " 📄 checkpoint-2400/training_args.bin\n", " 📄 checkpoint-2400/rng_state.pth\n", " 📄 checkpoint-2400/trainer_state.json\n", " 📄 checkpoint-2400/scaler.pt\n", " 📄 checkpoint-2400/model.safetensors\n", " 📄 checkpoint-2400/optimizer.pt\n", " 📄 checkpoint-2400/scheduler.pt\n", " 📄 checkpoint-1600/config.json\n", " 📄 checkpoint-1600/training_args.bin\n", " 📄 checkpoint-1600/rng_state.pth\n", " 📄 checkpoint-1600/trainer_state.json\n", " 📄 checkpoint-1600/scaler.pt\n", " 📄 checkpoint-1600/model.safetensors\n", " 📄 checkpoint-1600/optimizer.pt\n", " 📄 checkpoint-1600/scheduler.pt\n", " 📄 runs/Oct08_10-54-02_751ad0654dca/events.out.tfevents.1759920849.751ad0654dca.1414.0\n", " 📄 checkpoint-800/config.json\n", " 📄 checkpoint-800/training_args.bin\n", " 📄 checkpoint-800/rng_state.pth\n", " 📄 checkpoint-800/trainer_state.json\n", " 📄 checkpoint-800/scaler.pt\n", " 📄 checkpoint-800/model.safetensors\n", " 📄 checkpoint-800/optimizer.pt\n", " 📄 checkpoint-800/scheduler.pt\n", " Creating finbert_model.zip...\n", "📋 Files in finbert_model.zip:\n", " 📄 vocab.txt\n", " 📄 special_tokens_map.json\n", " 📄 tokenizer.json\n", " 📄 config.json\n", " 📄 label_encoder.pkl\n", " 📄 training_args.bin\n", " 📄 model.safetensors\n", " 📄 tokenizer_config.json\n", " 📄 checkpoint-1910/config.json\n", " 📄 checkpoint-1910/training_args.bin\n", " 📄 checkpoint-1910/rng_state.pth\n", " 📄 checkpoint-1910/trainer_state.json\n", " 📄 checkpoint-1910/scaler.pt\n", " 📄 checkpoint-1910/model.safetensors\n", " 📄 checkpoint-1910/optimizer.pt\n", " 📄 checkpoint-1910/scheduler.pt\n", " 📄 checkpoint-2865/config.json\n", " 📄 checkpoint-2865/training_args.bin\n", " 📄 checkpoint-2865/rng_state.pth\n", " 📄 checkpoint-2865/trainer_state.json\n", " 📄 checkpoint-2865/scaler.pt\n", " 📄 checkpoint-2865/model.safetensors\n", " 📄 checkpoint-2865/optimizer.pt\n", " 📄 checkpoint-2865/scheduler.pt\n", " 📄 checkpoint-955/config.json\n", " 📄 checkpoint-955/training_args.bin\n", " 📄 checkpoint-955/rng_state.pth\n", " 📄 checkpoint-955/trainer_state.json\n", " 📄 checkpoint-955/scaler.pt\n", " 📄 checkpoint-955/model.safetensors\n", " 📄 checkpoint-955/optimizer.pt\n", " 📄 checkpoint-955/scheduler.pt\n", "\n", " Model sizes:\n", " Emotion model: 2050.4 MB\n", " FinBERT model: 3522.9 MB\n", "\n", " Models packaged successfully!\n" ] } ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "import os\n", "\n", "print(\"⬇️ DOWNLOADING YOUR TRAINED MODELS...\")\n", "print(\"=\"*50)\n", "\n", "# Download model files\n", "print(\"📥 Downloading emotion model...\")\n", "files.download(\"emotion_model.zip\")\n", "\n", "print(\"📥 Downloading financial model...\")\n", "files.download(\"finbert_model.zip\")\n", "\n", "# Download datasets (backup)\n", "print(\"📥 Downloading datasets...\")\n", "files.download(\"emotion_dataset.csv\")\n", "files.download(\"financial_dataset.csv\")\n", "\n", "print(\"\\n🎉 ALL DOWNLOADS COMPLETE!\")\n", "print(\"=\"*40)\n", "\n", "print(\"\"\"\n", "📁 You now have:\n", " ✅ emotion_model.zip → Extract to Models/sentiment_model_distilbert/\n", " ✅ finbert_model.zip → Extract to finance/finbert_large_emotion_model/\n", " ✅ emotion_dataset.csv → Your emotion training data\n", " ✅ financial_dataset.csv → Your financial training data\n", "\n", "🚀 Next Steps:\n", " 1. Extract the zip files to your project directories\n", " 2. Run: streamlit run app_fixed.py\n", " 3. You should see: ✅ Emotion model loaded & ✅ FinBERT model loaded\n", " 4. Enjoy your professional AI sentiment analysis! 🎭📈\n", "\n", "💡 Integration Guide:\n", " - Emotion model: trained on 6 emotions (joy, sadness, anger, fear, love, surprise)\n", " - Financial model: trained on 3 sentiments (positive, negative, neutral)\n", " - Both models use the latest transformers and are production-ready!\n", "\"\"\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 503 }, "id": "NZ-2iW5AmHaU", "outputId": "b4c3c097-95d1-4771-b53e-50ccc667f8a3" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "⬇️ DOWNLOADING YOUR TRAINED MODELS...\n", "==================================================\n", "📥 Downloading emotion model...\n" ] }, { "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 \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_19ecb151-3d67-4c47-8c0c-528c2183573f\", \"emotion_model.zip\", 2150015903)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "📥 Downloading financial model...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${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_b861f191-bd2c-4ee8-a7a1-b46521b8759f\", \"finbert_model.zip\", 3694018995)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "📥 Downloading datasets...\n" ] }, { "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 \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_a6a3ce0f-5427-438a-bd25-439d284d477b\", \"emotion_dataset.csv\", 1597544)" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${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_39ceecea-c09d-43c7-b71f-62cfb0122d9d\", \"financial_dataset.csv\", 858645)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "🎉 ALL DOWNLOADS COMPLETE!\n", "========================================\n", "\n", "📁 You now have:\n", " ✅ emotion_model.zip → Extract to Models/sentiment_model_distilbert/\n", " ✅ finbert_model.zip → Extract to finance/finbert_large_emotion_model/\n", " ✅ emotion_dataset.csv → Your emotion training data\n", " ✅ financial_dataset.csv → Your financial training data\n", "\n", "🚀 Next Steps:\n", " 1. Extract the zip files to your project directories\n", " 2. Run: streamlit run app_fixed.py\n", " 3. You should see: ✅ Emotion model loaded & ✅ FinBERT model loaded\n", " 4. Enjoy your professional AI sentiment analysis! 🎭📈\n", "\n", "💡 Integration Guide:\n", " - Emotion model: trained on 6 emotions (joy, sadness, anger, fear, love, surprise)\n", " - Financial model: trained on 3 sentiments (positive, negative, neutral)\n", " - Both models use the latest transformers and are production-ready!\n", "\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "_Z7bNz7vnmMJ" }, "execution_count": null, "outputs": [] } ] }