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the HuggingFace library. They can build applications for text analysis, sentiment analysis, and more." ], "metadata": { "id": "6XCoL-qbihG9" } }, { "cell_type": "markdown", "source": [ "# 1. Preprocessing" ], "metadata": { "id": "7YjmjZhYMXMu" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e8bTqtp8fXCT", "outputId": "c1252d54-274f-4900-adb1-6cdca342466f", "colab": { "base_uri": "https://localhost:8080/", "height": 74 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving TestPlay_modified_modified.csv to TestPlay_modified_modified.csv\n" ] } ], "source": [ "from google.colab import files\n", "\n", "uploaded = files.upload()" ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "# Replace 'your_csv_file.csv' with the path to your CSV file\n", "csv_file_path = 'TestPlay_modified_modified.csv'\n", "\n", "# Read the CSV file\n", "data = pd.read_csv(csv_file_path)\n", "\n", "# Display the DataFrame\n", "data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "jQGuCqgoMQFb", "outputId": "95fc2afd-d931-45ac-b249-c4831cd92d9b" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Character Line Gender Age \\\n", "0 NARRATOR HARPER sits at a table alone in a room. She is... NaN NaN \n", "1 FATHER Hi, Harper. I’m really happy you came. NaN NaN \n", "2 HARPER Happy Father’s Day. NaN NaN \n", "3 FATHER Thanks… wow, you’re growing fast. How long has... NaN NaN \n", "4 HARPER It was Christmas. NaN NaN \n", ".. ... ... ... ... \n", "59 FATHER Never do anything that keeps you from giving y... NaN NaN \n", "60 HARPER I will. NaN NaN \n", "61 FATHER Goodbye, Harper. NaN NaN \n", "62 HARPER Goodbye, Daddy. NaN NaN \n", "63 NARRATOR They stand and look like they want to hug. Lig... NaN NaN \n", "\n", " Emotion Characteristic \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", ".. ... ... \n", "59 NaN NaN \n", "60 NaN NaN \n", "61 NaN NaN \n", "62 NaN NaN \n", "63 NaN NaN \n", "\n", "[64 rows x 6 columns]" ], "text/html": [ "\n", "
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CharacterLineGenderAgeEmotionCharacteristic
0NARRATORHARPER sits at a table alone in a room. She is...NaNNaNNaNNaN
1FATHERHi, Harper. I’m really happy you came.NaNNaNNaNNaN
2HARPERHappy Father’s Day.NaNNaNNaNNaN
3FATHERThanks… wow, you’re growing fast. How long has...NaNNaNNaNNaN
4HARPERIt was Christmas.NaNNaNNaNNaN
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59FATHERNever do anything that keeps you from giving y...NaNNaNNaNNaN
60HARPERI will.NaNNaNNaNNaN
61FATHERGoodbye, Harper.NaNNaNNaNNaN
62HARPERGoodbye, Daddy.NaNNaNNaNNaN
63NARRATORThey stand and look like they want to hug. Lig...NaNNaNNaNNaN
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Gender Prediction" ], "metadata": { "id": "3RV2aSryMeo0" } }, { "cell_type": "markdown", "source": [ "**accelearte**: a library that enables the same PyTorch code to be run across any distributed configuration\n", "\n", "**transformers**: provides APIs and tools to easily download and train state-of-the-art pretrained models" ], "metadata": { "id": "TYqtj0GhiwBg" } }, { "cell_type": "code", "source": [ "!pip install accelerate==0.21.0\n", "!pip install transformers==4.30.1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6PJnNTykfn6C", "outputId": "28f0532a-28dc-4443-e212-cc254ac669ce" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting accelerate==0.21.0\n", " Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 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tokenizers-0.15.0:\n", " Successfully uninstalled tokenizers-0.15.0\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 4.35.2\n", " Uninstalling transformers-4.35.2:\n", " Successfully uninstalled transformers-4.35.2\n", "Successfully installed tokenizers-0.13.3 transformers-4.30.1\n" ] } ] }, { "cell_type": "code", "source": [ "import accelerate\n", "import transformers\n", "\n", "transformers.__version__, accelerate.__version__" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NXQUdisQgNp2", "outputId": "49381f1d-cfe2-4ec1-decf-648b8b841e2e" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "('4.30.1', '0.21.0')" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n", "import torch\n", "\n", "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n", "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")" ], "metadata": { "id": "fd8OAMAFNIQO", "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "1bf890d5dab44fbc852ef6401512b3ad", "1f50528dd1034dea88882050eb365da5", "6e19dc5fb5674e42af7ab4abe8574bc8", "d5328f157da843ccae628af60bd16d7d", "4758beb83d7c4b59b2e0a0968eeaabb6", "fd56d303c1584a6a8ba8f5eadc3976c0", "caf1012373334ecb83fe665e841728d8", "0c3ec4704b6d4e0fad019581244128e3", "05aa48cf8a0f481f907aa154b1ad9751", "ad95621d9d8548bebfc4f4adaf560faf", "d6855e94498b4d2b85d96e5cacded45d", "1a5a66c32ecf4dbba265f8a9368a1050", "89008fb936df454c9de227190de7664c", "dfd92d71ecc5476e84844dfd27631840", "b26c077a46334ef89274b05edd33aaee", "74380dec66f240ec8f1bbd2f79091602", "29af598da102400cb3a1baeadd74fef7", "04cbc00a0bfd461ab1d8b0e1600d4134", "2bce900de0ae4329ae0d874f93451e35", "3b490343ee834936915d96a90d82672e", "ef59e3fd28e94ec7a84fef5815ec338b", "7770a4234b1541e2bcbb4797783dbf4c", "abc07d7c3634411cb17216ebab1e2571", 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CharacterLineGenderAgeEmotionCharacteristic
0NARRATORHARPER sits at a table alone in a room. She is...neutralNaNNaNNaN
1FATHERHi, Harper. I’m really happy you came.maleNaNNaNNaN
2HARPERHappy Father’s Day.femaleNaNNaNNaN
3FATHERThanks… wow, you’re growing fast. How long has...maleNaNNaNNaN
4HARPERIt was Christmas.femaleNaNNaNNaN
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59FATHERNever do anything that keeps you from giving y...maleNaNNaNNaN
60HARPERI will.femaleNaNNaNNaN
61FATHERGoodbye, Harper.maleNaNNaNNaN
62HARPERGoodbye, Daddy.femaleNaNNaNNaN
63NARRATORThey stand and look like they want to hug. Lig...neutralNaNNaNNaN
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64 rows × 6 columns

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CharacterLineGenderAgeEmotionCharacteristic
0NARRATORHARPER sits at a table alone in a room. She is...neutralNaNNEGATIVENaN
1FATHERHi, Harper. I’m really happy you came.maleNaNPOSITIVENaN
2HARPERHappy Father’s Day.femaleNaNPOSITIVENaN
3FATHERThanks… wow, you’re growing fast. How long has...maleNaNPOSITIVENaN
4HARPERIt was Christmas.femaleNaNPOSITIVENaN
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59FATHERNever do anything that keeps you from giving y...maleNaNNEGATIVENaN
60HARPERI will.femaleNaNPOSITIVENaN
61FATHERGoodbye, Harper.maleNaNNEGATIVENaN
62HARPERGoodbye, Daddy.femaleNaNNEGATIVENaN
63NARRATORThey stand and look like they want to hug. Lig...neutralNaNPOSITIVENaN
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HuggingFace" ], "metadata": { "id": "KySjLtQJKYei" } }, { "cell_type": "code", "source": [ "!pip install datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Sn8IxNxHNqY-", "outputId": "fdd36212-e743-4d67-c8ac-7745a19e50e1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n", "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n", "Collecting pyarrow-hotfix (from datasets)\n", " Downloading pyarrow_hotfix-0.5-py3-none-any.whl (7.8 kB)\n", "Collecting dill<0.3.8,>=0.3.0 (from 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\"sentences_allagree\")\n", "\n", "# Convert the dataset to a pandas DataFrame\n", "df = pd.DataFrame(dataset['train'])\n", "df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 584, "referenced_widgets": [ "b1429d0b096f41dfabc4be7b64d6ebda", "f62fec920bea47c28a0dbc93ef35a7c0", "f39262d95755482f91990809d3a8270b", "048016855efa422fb6863d75b7de0f35", "51411a445a3c4abd9755518ca06b607b", "0428702420c740048af237d1fbc4ce7b", "2d20c34f5a614a8391493adced7396c2", "b63811d1ce2c44dda2d97ef37db0dd90", "995c4f218ea147e9903d455451a7d2c1", "bc1e953d105c45549b8bc427de303c41", "88b626b7c2984895837cfed98e905f79", "8348bfaf711d44769679fea26d7d381f", "f08054c7244e465996d99cd9a67423d9", "f07be2df887e45b6b71037b926e927ed", "81b7e9882c8e4716a0a8431d933077eb", "e3f55f0634a44f14a27b9d65c53d68bd", "ce7ffee107b3495db67ad7d7a347dca4", "eead13a03f474d798f5c44c0273a00a1", "2b3a974c26514000a64a244e78abeea3", "2be63c5bbb6f4d1c865865cad0c10db5", "dd649aabfe7a4fec925f08929dfe3a56", "f8f36178f9d740a4ae914b3acf2b9fc1", "09b4375096454ae08958937ce4aa1e4e", "0b671c52585a4e2baa0fbd3d407909a9", "daebb32b6706466291aa079cf95c95fe", "1893b94a33d94bde8d4a90638aef8fdc", "3ceb0400849944cd838582473e0f7494", "fb8b4f0389544f0086948ed7ba7a9658", "208b91f8c51749d985cfc40f9b8fd0fa", "139fcbf695a7459dacba0d76135e5f06", "bfae545c86cc485fadb6f7f2d46980d7", "f853b13f643f40e4bfbb28c4c09b8640", "e70c0764a0174607a53022d233e13082", "d208a8b071034d51b7bfaa456225daa1", "e64d7b6ad37a417a87e023abc3c31bc3", "b0d48adbb3a945088a21d6d701c763d4", "41eb5ff04e3f484091797e1bea1406c3", "448f1c79b81244719aec0b4de355d42e", "4a4467eaf723497587ae72a31ffcebd2", "3350ec8ee2ec4521840fdcee97e1eefc", "318d539d94fd4173a2738f9500e9158f", "aa2d75b119ae451ebba4c2c98b142f28", "2d89acf94b0d43778344d5c390e92f68", "b447e275cd6c42bebcce2f44b21a7c07", "16efd37b82ee4fed9e6d60977305e88e", "5d9ee079547e42c29fa06cf885a523d0", "afac19b17b544cdaaf3ff2e99cb9e0bc", "83651b9a34a741169a1baefb2d0ef5df", "5e114408bf5146468cbd6afe53733a82", "321f5173a67647cf9154f7e28720d857", "313138bd468b4b75a0b2729a15ce0c3f", "565fdca991e64e6895d507a5e27a6132", "4eb5bdecaa154180a99612d0dec8f5b3", "1f034849b26e45a5a03c1fd7585cfba0", "97b85a70ce0d4102ae72cc64bbeb8e7b" ] }, "id": "XvgC_E3zNUOa", "outputId": "94c62dca-3a5e-4472-ca26-c9451f455c3d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/6.04k [00:00\n", "
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sentencelabel
0According to Gran , the company has no plans t...1
1For the last quarter of 2010 , Componenta 's n...2
2In the third quarter of 2010 , net sales incre...2
3Operating profit rose to EUR 13.1 mn from EUR ...2
4Operating profit totalled EUR 21.1 mn , up fro...2
.........
2259Operating result for the 12-month period decre...0
2260HELSINKI Thomson Financial - Shares in Cargote...0
2261LONDON MarketWatch -- Share prices ended lower...0
2262Operating profit fell to EUR 35.4 mn from EUR ...0
2263Sales in Finland decreased by 10.5 % in Januar...0
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Kaggle" ], "metadata": { "id": "D0NoQvnBKdyd" } }, { "cell_type": "code", "source": [ "!pip install kaggle\n", "\n", "import os\n", "\n", "# Set your Kaggle API credentials\n", "os.environ[\"KAGGLE_USERNAME\"] = \"ddiddu\"\n", "os.environ[\"KAGGLE_KEY\"] = \"95135e8fe8461f311642da97bc623dd8\"\n", "\n", "# Download the dataset using the Kaggle API\n", "!kaggle datasets download -d jp797498e/twitter-entity-sentiment-analysis\n", "!ls" ], "metadata": { "id": "L3olv5z6ttEh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Unzip the downloaded dataset\n", "!unzip twitter-entity-sentiment-analysis.zip" ], "metadata": { "id": "XltxaCsktuoc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df = pd.read_csv(\"twitter_training.csv\")\n", "test_df = pd.read_csv(\"twitter_validation.csv\")\n", "\n", "# Assuming you have a DataFrame df with the data\n", "# Rename columns\n", "df.rename(columns={'2401': 'Tweet ID', 'Borderlands': 'entity', 'Positive': 'label', 'im getting on borderlands and i will murder you all ,': 'sentence'}, inplace=True)\n", "test_df.rename(columns={'3364': 'Tweet ID', 'BorderFacebooklands': 'entity', 'Irrelevant': 'label', 'I mentioned on Facebook that I was struggling for motivation to go for a run the other day, which has been translated by Tom’s great auntie as ‘Hayley can’t get out of bed’ and told to his grandma, who now thinks I’m a lazy, terrible person 🤣': 'sentence'}, inplace=True)\n", "\n", "# Replace label values\n", "df['label'] = df['label'].replace({'Positive': 2, 'Neutral': 1, 'Negative': 0})\n", "test_df['label'] = test_df['label'].replace({'Positive': 2, 'Neutral': 1, 'Negative': 0})\n", "\n", "# Filter out rows where 'label' is not \"Irrelevant\"\n", "df = df[df['label'] != 'Irrelevant']\n", "test_df = test_df[test_df['label'] != 'Irrelevant']\n", "\n", "# Reset the index of the DataFrame\n", "df.reset_index(drop=True, inplace=True)\n", "test_df.reset_index(drop=True, inplace=True)\n", "\n", "test_df" ], "metadata": { "id": "s61X3J5PvgSW" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df.groupby(['label']).size()" ], "metadata": { "id": "Jyh7cqtNwNWB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Load and preprocess your Kaggle dataset\n", "train_df, validation_df = train_test_split(df, test_size=0.2, random_state=42)\n", "validation_df" ], "metadata": { "id": "nfsy1TB4t01Q" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 3.2.2. Remove Unnecessary Words" ], "metadata": { "id": "Kn0Kw9gblEed" } }, { "cell_type": "code", "source": [ "# import nltk\n", "# nltk.download('book')\n", "# from nltk.book import *\n", "# from nltk.tokenize import sent_tokenize, word_tokenize" ], "metadata": { "id": "k9qCEyVolEBI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# def tokenize_POS(paragraph):\n", "# words = word_tokenize(paragraph)\n", "# tagged_words = nltk.pos_tag(words)\n", "\n", "# # Remove not important types of words\n", "# excluded_tags = ['CC', 'DT', 'IN', 'TO', 'PRP', 'PRP$', 'MD', 'WP', 'WP$', 'WRB']\n", "# filtered_words = [word for word, pos in tagged_words if pos not in excluded_tags]\n", "\n", "# return ' '.join(filtered_words)\n", "\n", "# tokenize_POS(df['sentence'][0])" ], "metadata": { "id": "dguyyS03m89Z" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# # Apply the function to each row in the 'sentence' column of the DataFrame\n", "# df['filtered_sentence'] = df['sentence'].apply(tokenize_POS)" ], "metadata": { "id": "zadFiU8gnZs_" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 3.3.3. Training\n", "* v1 {'eval_loss': 0.3472066819667816,\n", " 'eval_runtime': 1.6455,\n", " 'eval_samples_per_second': 137.95,\n", " 'eval_steps_per_second': 9.116,\n", " 'epoch': 10.0}\n", "* v2 {'eval_loss': 0.22732013463974,\n", " 'eval_runtime': 1.6519,\n", " 'eval_samples_per_second': 137.414,\n", " 'eval_steps_per_second': 9.08,\n", " 'epoch': 10.0}\n", "* v3\n" ], "metadata": { "id": "0fdshJY-lQv8" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "id": "lst2lRrHp4IY", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "dd831f46-b6d7-4176-a63f-320de6dc59c7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import Dataset\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n", "\n", "# Check if GPU is available and set the device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(\"Using device:\", device)\n", "\n", "# Convert DataFrame to Hugging Face Dataset\n", "def df_to_dataset(df):\n", " return Dataset.from_pandas(df)\n", "\n", "train_dataset = df_to_dataset(train_df)\n", "validation_dataset = df_to_dataset(validation_df)\n", "test_dataset = df_to_dataset(test_df)\n", "\n", "# Load a pretrained model and tokenizer\n", "model_name = \"bert-base-uncased\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3) # 3 labels: positive, negative, neutral\n", "model.to(device) # Move the model to GPU\n", "\n", "# Tokenize the datasets\n", "def tokenize_function(examples):\n", " return tokenizer(examples['sentence'], padding='max_length', truncation=True)\n", "\n", "print(train_dataset[0])\n", "print(validation_dataset[0])\n", "print(test_dataset[0])\n", "\n", "tokenized_train = train_dataset.map(tokenize_function, batched=True)\n", "tokenized_validation = validation_dataset.map(tokenize_function, batched=True)\n", "tokenized_test = test_dataset.map(tokenize_function, batched=True)\n", "\n", "# Define training arguments\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\",\n", " num_train_epochs=10,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " warmup_steps=500,\n", " weight_decay=0.01,\n", " evaluation_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True\n", ")\n", "\n", "# Initialize Trainer\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=tokenized_train,\n", " eval_dataset=tokenized_validation,\n", ")\n", "\n", "# Train the model\n", "trainer.train()\n", "\n", "# Evaluate the model\n", "trainer.evaluate(tokenized_test)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 771, "referenced_widgets": [ "3cbc817447ac43a58bd81496e19b5844", "ab634b7974be45fa916182d08f9e9af2", "17df3e11160d4027a4a28c8b8986fc8f", "bf09e96cfc15482fa5f57224e9982598", "d1ad9fd445ea4b568272b4a760c27e80", "7894615e7c644af686e16ac5f0180a20", "73d1addcb74148fa962e5ace76612f57", "8321080723214edb9bf39b3d2d8f93b1" ] }, "id": "GjtrNjm_esxa", "outputId": "52ce177d-8a04-4aa7-9aed-97e549d61443" }, "execution_count": null, "outputs": [ { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ "Using device: cuda\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3cbc817447ac43a58bd81496e19b5844", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/28.0 [00:00\n", " \n", " \n", " [ 158/1140 03:55 < 24:40, 0.66 it/s, Epoch 1.38/10]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation Loss
1No log0.512281

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EpochTraining LossValidation Loss
1No log0.512281
2No log0.169869
3No log0.027977
4No log0.109293
50.3237000.014466

" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "model_path = \"./financial_phrasebank_model_v1\"\n", "\n", "# Save the model and tokenizer\n", "model.save_pretrained(model_path)\n", "tokenizer.save_pretrained(model_path)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Jw0KKlGezL4", "outputId": "28c2e2b1-7ff0-457b-9ffd-4466a30b18c6" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "('./financial_phrasebank_model_v1/tokenizer_config.json',\n", " './financial_phrasebank_model_v1/special_tokens_map.json',\n", " './financial_phrasebank_model_v1/vocab.txt',\n", " './financial_phrasebank_model_v1/added_tokens.json',\n", " './financial_phrasebank_model_v1/tokenizer.json')" ] }, "metadata": {}, "execution_count": 16 } ] }, { "cell_type": "code", "source": [ "from transformers import pipeline\n", "\n", "# Path to your saved model\n", "model_path = \"./financial_phrasebank_model_v1\"\n", "\n", "# Initialize sentiment analysis pipeline with your model\n", "sentiment_pipeline = pipeline('sentiment-analysis', model=model_path, tokenizer=model_path)" ], "metadata": { "id": "nnCF4elDkffI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def addEmotionColumn(df):\n", " emotions = []\n", " for line in df['Line']:\n", " sentiment_result = sentiment_pipeline(line)\n", " emotion = sentiment_result[0]['label']\n", " emotions.append(emotion)\n", "\n", " df['Emotion'] = emotions\n", " return df\n", "\n", "# Apply to your data\n", "data = addEmotionColumn(data)\n", "\n", "# Assuming your DataFrame is named 'data'\n", "emotion_mapping = {'LABEL_0': 'negative', 'LABEL_1': 'neutral', 'LABEL_2': 'positive'}\n", "data['Emotion'] = data['Emotion'].map(emotion_mapping)\n", "\n", "data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 720 }, "id": "RdL22VHqkn78", "outputId": "52312594-10f7-4dc0-f713-954d977b12d0" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Character Line Gender Age \\\n", "0 NARRATOR HARPER sits at a table alone in a room. She is... neutral NaN \n", "1 FATHER Hi, Harper. I’m really happy you came. male NaN \n", "2 HARPER Happy Father’s Day. female NaN \n", "3 FATHER Thanks… wow, you’re growing fast. How long has... male NaN \n", "4 HARPER It was Christmas. female NaN \n", ".. ... ... ... ... \n", "59 FATHER Never do anything that keeps you from giving y... male NaN \n", "60 HARPER I will. female NaN \n", "61 FATHER Goodbye, Harper. male NaN \n", "62 HARPER Goodbye, Daddy. female NaN \n", "63 NARRATOR They stand and look like they want to hug. Lig... neutral NaN \n", "\n", " Emotion Characteristic \n", "0 neutral NaN \n", "1 positive NaN \n", "2 positive NaN \n", "3 positive NaN \n", "4 neutral NaN \n", ".. ... ... \n", "59 neutral NaN \n", "60 neutral NaN \n", "61 neutral NaN \n", "62 neutral NaN \n", "63 neutral NaN \n", "\n", "[64 rows x 6 columns]" ], "text/html": [ "\n", "

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CharacterLineGenderAgeEmotionCharacteristic
0NARRATORHARPER sits at a table alone in a room. She is...neutralNaNneutralNaN
1FATHERHi, Harper. I’m really happy you came.maleNaNpositiveNaN
2HARPERHappy Father’s Day.femaleNaNpositiveNaN
3FATHERThanks… wow, you’re growing fast. How long has...maleNaNpositiveNaN
4HARPERIt was Christmas.femaleNaNneutralNaN
.....................
59FATHERNever do anything that keeps you from giving y...maleNaNneutralNaN
60HARPERI will.femaleNaNneutralNaN
61FATHERGoodbye, Harper.maleNaNneutralNaN
62HARPERGoodbye, Daddy.femaleNaNneutralNaN
63NARRATORThey stand and look like they want to hug. Lig...neutralNaNneutralNaN
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64 rows × 6 columns

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\n" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "code", "source": [ "!zip -r financial_phrasebank_model_v1.zip financial_phrasebank_model_v1/" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "poNaAmIlqZeA", "outputId": "9b326e83-b714-492d-af27-b21a0180af15" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " adding: financial_phrasebank_model_v1/ (stored 0%)\n", " adding: financial_phrasebank_model_v1/pytorch_model.bin (deflated 7%)\n", " adding: financial_phrasebank_model_v1/vocab.txt (deflated 53%)\n", " adding: financial_phrasebank_model_v1/config.json (deflated 51%)\n", " adding: financial_phrasebank_model_v1/tokenizer_config.json (deflated 43%)\n", " adding: financial_phrasebank_model_v1/special_tokens_map.json (deflated 42%)\n", " adding: financial_phrasebank_model_v1/tokenizer.json (deflated 71%)\n" ] } ] }, { "cell_type": "code", "source": [ "!mv financial_phrasebank_model_v1.zip /content/drive/MyDrive/" ], "metadata": { "id": "2JQUYZRtqcAj" }, "execution_count": null, "outputs": [] } ] }