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  1. LICENSE +21 -0
  2. README.md +73 -9
  3. app/__init__.py +0 -0
  4. app/database.py +0 -0
  5. app/main.py +0 -0
  6. app/ml_model.py +0 -0
  7. app/models.py +0 -0
  8. app/schemas.py +0 -0
  9. notebooks/data_exploration.ipynb +2126 -0
  10. requirements.txt +0 -0
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ MIT License
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+
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+ Copyright (c) 2026 Muyiwa J. Obadara
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ SOFTWARE.
README.md CHANGED
@@ -1,12 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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- title: Finbert Sentiment Api
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- emoji: 📊
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- colorFrom: blue
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- colorTo: green
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- sdk: docker
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- pinned: false
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- license: mit
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- short_description: Specialized sentiment analysis for financial news and report
 
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  ---
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # 📈 FinBERT: Real-Time Financial Sentiment Analysis
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+
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+ Machine Learning Pipeline, which analyses news headlines about finance and forecasts sentiments (Bullish, Bearish, and Neutral).
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+
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+ The project will train a BERT-based model with PyTorch, implement API prediction requests using FastAPI, and display visualization results on a React-Bootstrap web interface.
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+
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+ ### 🔗 Project Links
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+ * **Live Application:** [Link](https://portfolio-frontend-livid.vercel.app/projects/sentiment-analysis-with-bert)
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+ * **Frontend Code:** [Github](https://github.com/mobadara/portfolio-frontend/blob/main/src/pages/projects/FinBERT.jsx)
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+ * **Project Blog Post:** [Read the full write-up on Medium/Dev.to]
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+ * **Video Walkthrough:** [Watch the explanation on YouTube]
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+ * **Model Weights (Optional):** [Link to your Hugging Face model hub if uploaded]
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+
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+ ---
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+
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+ ## 🏗️ System Architecture
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+
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+ * **Machine Learning:** PyTorch, Hugging Face `transformers`, Financial PhraseBank Dataset
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+ * **Backend API:** Python, FastAPI, Uvicorn
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+ * **Frontend UI:** React, React-Bootstrap
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+ * **Database:** PostgreSQL (Neon/Supabase) via SQLAlchemy
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+
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  ---
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+
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+ ## 🧠 The Machine Learning Pipeline
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+
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+ The core of this application is a fine-tuned NLP model.
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+ 1. **Base Model:** `ProsusAI/finbert`
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+ 2. **Fine-tuning:** Conducted in Google Colab using a T4 GPU.
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+ 3. **Training Data:** The Kaggle Financial PhraseBank dataset.
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+ *(Check the `/notebooks` directory to see the complete PyTorch training loop, tokenization process, and evaluation metrics).*
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+
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  ---
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+ ## 🚀 How to Run Locally
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+
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+ ### 1. Clone the Repository
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+
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+ copy the command below and run it in your favourite terminal.
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+ ```bash
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+ git clone https://github.com/mobadara/finbert-sentiment-analyzer-api.git &&
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+ cd finbert-sentiment-analyzer-api.git
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+ ```
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+
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+ ### 2. Create a virtual environment
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+ ```
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+ python -m venv venv
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+ ```
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+
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+ ### 3. Activate the virtual environment
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+ #### a. On Linux/Mac
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+ ```bash
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+ source venv/bin/activate
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+ ```
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+
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+ #### b. On Windows
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+ ```bash
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+ venv\Scripts\activate
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+ ```
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+
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+ ### 4. Install Dependencies
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+ ### 5. Start the server
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+ ```bash
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+ uvicorn app.main:app --reload
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+ ```
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+
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+ ## 👨‍💻 Author:
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+ **Muyiwa J. Obadara**
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+
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+ [![LinkedIn](https://img.shields.io/badge/LinkedIn-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://linkedin.com/in/obadara-m)
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+ [![X / Twitter](https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge&logo=X&logoColor=white)](https://twitter.com/m_obadara)
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+ [![Portfolio](https://img.shields.io/badge/Portfolio-Navy_Blue?style=for-the-badge&logo=google-chrome&logoColor=white&color=navy)](https://portfolio-frontend-livid.vercel.app)
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {
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+ "id": "view-in-github",
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+ "colab_type": "text"
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+ },
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+ "source": [
1742
+ "<a href=\"https://colab.research.google.com/github/mobadara/finbert-sentiment-analyzer-api/blob/main/notebooks/data_exploration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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+ ]
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+ {
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+ "cell_type": "markdown",
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+ "source": [
1748
+ "# 📊 Exploratory Data Analysis (EDA): Financial PhraseBank\n",
1749
+ "\n",
1750
+ "**Project:** Full-Stack Financial NLP Pipeline (Part 1: Data Exploration) \n",
1751
+ "**Environment:** Google Colab (Standard/CPU or T4 GPU) \n",
1752
+ "**Dataset:** [Financial PhraseBank (Hugging Face)](https://huggingface.co/datasets/financial_phrasebank)\n",
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+ "\n",
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+ "---\n",
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+ "\n",
1756
+ "## 🎯 Notebook Objective\n",
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+ "\n",
1758
+ "1. **Analysis of Class Distribution:** How imbalanced is our dataset? We will map out the ratio between Bullish (positive), Bearish (negative), and Neutral headlines.\n",
1759
+ "\n",
1760
+ "2. **Token Length and Padding:** As transformer models tend to have strict token limits (typically 512 tokens), we will assess the number of characters and words in our sentences in order to determine the optimal max_length for our tokenizer, therefore optimising memory usage on the GPU in the future.\n",
1761
+ "\n",
1762
+ "3. **Linguistic Profiling:** Analysing which common financial keywords and n-grams exist in each sentiment class in order to understand how the experts classified/annotated the text.\n",
1763
+ "\n",
1764
+ "4. **Engineering the Data:** We will finalise the dataset's structure (address any null or anomalistic data) so that it can be ingested by our custom PyTorch DataLoader in the next phase.\n"
1765
+ ],
1766
+ "metadata": {
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+ "id": "Zu3lxShaUmua"
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+ }
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "source": [
1773
+ "## Setup"
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+ ],
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+ "metadata": {
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+ "id": "n77liDziXkPc"
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+ }
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 13,
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+ "metadata": {
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+ "id": "cIdc7O5zRDpG"
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+ },
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+ "outputs": [],
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+ "source": [
1787
+ "import pandas as pd\n",
1788
+ "from datasets import load_dataset"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
1794
+ "dataset = load_dataset(\"FinanceMTEB/financial_phrasebank\")"
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+ ],
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+ "metadata": {
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+ "id": "Pb9met8DX5nw",
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+ "outputId": "6425acb7-d76b-4fd7-f0ea-a6241b7dabbb",
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 177,
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+ "referenced_widgets": [
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+ "sample_data = dataset[\"train\"].to_pandas()\n",
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+ ],
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+ "metadata": {
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+ }
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+ },
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+ "execution_count": 14,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
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+ " text label_text label\n",
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+ "0 The Samsung Mobile Applications Store was laun... neutral 1\n",
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+ "1 F-Secure , a developer of security solutions a... neutral 1\n",
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+ "2 The company serves customers in various indust... neutral 1\n",
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+ ],
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+ "text/html": [
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+ "\n",
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+ " <div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ "</style>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " </thead>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <tr>\n",
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+ " <td>The company reported net sales of 302 mln euro...</td>\n",
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+ " <td>neutral</td>\n",
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+ " <td>1</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>Microsoft last week also issued the first patc...</td>\n",
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+ " <td>neutral</td>\n",
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+ " <td>1</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
2020
+ "</table>\n",
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+ "</div>\n",
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+ " <div class=\"colab-df-buttons\">\n",
2023
+ "\n",
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+ " <div class=\"colab-df-container\">\n",
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+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c74413c-185d-4488-9887-9b64d7c15536')\"\n",
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+ " title=\"Convert this dataframe to an interactive table.\"\n",
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+ " </svg>\n",
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+ " </button>\n",
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+ "\n",
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+ " <style>\n",
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+ " .colab-df-container {\n",
2036
+ " display:flex;\n",
2037
+ " gap: 12px;\n",
2038
+ " }\n",
2039
+ "\n",
2040
+ " .colab-df-convert {\n",
2041
+ " background-color: #E8F0FE;\n",
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+ " border: none;\n",
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+ " border-radius: 50%;\n",
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+ " cursor: pointer;\n",
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+ " padding: 0 0 0 0;\n",
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+ " width: 32px;\n",
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+ " }\n",
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+ "\n",
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+ " .colab-df-convert:hover {\n",
2053
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2054
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2055
+ " fill: #174EA6;\n",
2056
+ " }\n",
2057
+ "\n",
2058
+ " .colab-df-buttons div {\n",
2059
+ " margin-bottom: 4px;\n",
2060
+ " }\n",
2061
+ "\n",
2062
+ " [theme=dark] .colab-df-convert {\n",
2063
+ " background-color: #3B4455;\n",
2064
+ " fill: #D2E3FC;\n",
2065
+ " }\n",
2066
+ "\n",
2067
+ " [theme=dark] .colab-df-convert:hover {\n",
2068
+ " background-color: #434B5C;\n",
2069
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+ " }\n",
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+ "\n",
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+ " <script>\n",
2076
+ " const buttonEl =\n",
2077
+ " document.querySelector('#df-2c74413c-185d-4488-9887-9b64d7c15536 button.colab-df-convert');\n",
2078
+ " buttonEl.style.display =\n",
2079
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2080
+ "\n",
2081
+ " async function convertToInteractive(key) {\n",
2082
+ " const element = document.querySelector('#df-2c74413c-185d-4488-9887-9b64d7c15536');\n",
2083
+ " const dataTable =\n",
2084
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2085
+ " [key], {});\n",
2086
+ " if (!dataTable) return;\n",
2087
+ "\n",
2088
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
2089
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2090
+ " + ' to learn more about interactive tables.';\n",
2091
+ " element.innerHTML = '';\n",
2092
+ " dataTable['output_type'] = 'display_data';\n",
2093
+ " await google.colab.output.renderOutput(dataTable, element);\n",
2094
+ " const docLink = document.createElement('div');\n",
2095
+ " docLink.innerHTML = docLinkHtml;\n",
2096
+ " element.appendChild(docLink);\n",
2097
+ " }\n",
2098
+ " </script>\n",
2099
+ " </div>\n",
2100
+ "\n",
2101
+ "\n",
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+ " </div>\n",
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+ ],
2105
+ "application/vnd.google.colaboratory.intrinsic+json": {
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+ "type": "dataframe",
2107
+ "variable_name": "sample_data",
2108
+ "summary": "{\n \"name\": \"sample_data\",\n \"rows\": 1264,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1263,\n \"samples\": [\n \"R&D Loan ) .\",\n \"Mr. Mikko Saavalainen , head of Comptel 's Global Sales concludes : `` Gibtelecom provides a perfect illustration of the variety of business , technical and regulatory challenges operators are facing in their OSS today .\",\n \"The decision reflects the underutilisation of the line , which produces nonwovens used in medical and wipes applications as well as for the automotive industry .\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label_text\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"positive\",\n \"negative\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 2,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n 2,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
2109
+ }
2110
+ },
2111
+ "metadata": {},
2112
+ "execution_count": 14
2113
+ }
2114
+ ]
2115
+ },
2116
+ {
2117
+ "cell_type": "code",
2118
+ "source": [],
2119
+ "metadata": {
2120
+ "id": "qgenw3blcH7q"
2121
+ },
2122
+ "execution_count": null,
2123
+ "outputs": []
2124
+ }
2125
+ ]
2126
+ }
requirements.txt ADDED
File without changes