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* Fix: Broken links in README.rst banner text [skip ci] Signed-off-by: Matthew Watkins <[email protected]> * Chore: pre-commit autoupdate --------- Signed-off-by: Matthew Watkins <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
os-climate · Jul 2, 2024 · 7d4d882 · 7d4d882
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diff --git a/README.rst b/README.rst
@@ -1,7 +1,7 @@
 
-[IMPORTANT]
+💬 Important
 
-On June 26 2024, Linux Foundation announced the merger of its financial services umbrella, the Fintech Open Source Foundation ([FINOS](https://finos.org)), with OS-Climate, an open source community dedicated to building data technologies, modeling, and analytic tools that will drive global capital flows into climate change mitigation and resilience; OS-Climate projects are in the process of transitioning to the [FINOS governance framework](https://community.finos.org/docs/governance); read more on [finos.org/press/finos-join-forces-os-open-source-climate-sustainability-esg](https://finos.org/press/finos-join-forces-os-open-source-climate-sustainability-esg)
+On June 26 2024, Linux Foundation announced the merger of its financial services umbrella, the Fintech Open Source Foundation (`FINOS <https://finos.org>`_), with OS-Climate, an open source community dedicated to building data technologies, modelling, and analytic tools that will drive global capital flows into climate change mitigation and resilience; OS-Climate projects are in the process of transitioning to the `FINOS governance framework <https://community.finos.org/docs/governance>`_; read more on `finos.org/press/finos-join-forces-os-open-source-climate-sustainability-esg <https://finos.org/press/finos-join-forces-os-open-source-climate-sustainability-esg>`_
 
 
 .. image:: https://img.shields.io/badge/OS-Climate-blue

diff --git a/src/osc_transformer_based_extractor/README.md b/src/osc_transformer_based_extractor/README.md
@@ -1,52 +1,55 @@
----
-
 # Relevance Detector
 
 This folder contains a set of scripts and notebooks designed to process data, train a sentence transformer model, and perform inferences to detect the relevance of folder contents. Below is a detailed description of each file and folder included in this repository.
 
 ## How to Use This Repository
 
 1. **Prepare Training Data**:
+
    - One must have data from the curator module, which is used for training of the model. The data from the curator module is a CSV file as follows:
 
-   ### Example Snippet
+### Example Snippet
 
-   | question                      | context                                                                                                                   | company | source_file                       | source_page | kpi_id | year | answer       | data_type | relevant_paragraphs                            | annotator          | Index | label |
-   |-------------------------------|----------------------------------------------------------------------------------------------------------------------------|---------|-----------------------------------|-------------|--------|------|--------------|-----------|------------------------------------------------|---------------------|-------|-------|
-   | What is the company name?     | The Company is exposed to a risk of by losses counterparties their contractual financial obligations when due, and in particular depends on the reliability of banks the Company deposits its available cash. | NOVATEK | 04_NOVATEK_AR_2016_ENG_11.pdf | ['0']       | 0      | 2016 | PAO NOVATEK  | TEXT      | ["PAO NOVATEK ANNUAL REPORT 2016"]            | train_anno_large.xlsx | 1022  | 0     |
+   | question                  | context                                                                                                                                                                                                       | company | source_file                   | source_page | kpi_id | year | answer      | data_type | relevant_paragraphs                | annotator             | Index | label |
+   | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | ----------------------------- | ----------- | ------ | ---- | ----------- | --------- | ---------------------------------- | --------------------- | ----- | ----- |
+   | What is the company name? | The Company is exposed to a risk of by losses counterparties their contractual financial obligations when due, and in particular depends on the reliability of banks the Company deposits its available cash. | NOVATEK | 04_NOVATEK_AR_2016_ENG_11.pdf | ['0']       | 0      | 2016 | PAO NOVATEK | TEXT      | ["PAO NOVATEK ANNUAL REPORT 2016"] | train_anno_large.xlsx | 1022  | 0     |
 
    - If you have CSV data from the curator module, run `make_training_data_from_curator.py` to process and save it in the `Data` folder.
    - Alternatively, you can use `make_sample_training_data.ipynb` to generate sample data from a sample CSV file.
 
-
 2. **Train the Model**:
+
    - Use `train_sentence_transformer.ipynb` or `train_sentence_transformer.py` to train a sentence transformer model with the processed data from the `Data` folder and save it locally. Follow the steps in the notebook or script to configure and start the training process.
 
    - To train the model using function calling
-      ```python
+
+     ```python
      from train_sentence_transformer import fine_tune_model
      fine_tune_model(
-         data_path="data/train_data.csv",
-         model_name="sentence-transformers/all-MiniLM-L6-v2",
-         num_labels=2,
-         max_length=512,
-         epochs=2,
-         batch_size=4,
-         output_dir="./saved_models_during_training",
-         save_steps=500
+        data_path="data/train_data.csv",
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        num_labels=2,
+        max_length=512,
+        epochs=2,
+        batch_size=4,
+        output_dir="./saved_models_during_training",
+        save_steps=500
      )
      ```
+
      **Parameters**:
-       - `data_path (str)`: Path to the training data CSV file.
-       - `model_name (str)`: Pre-trained model name from HuggingFace.
-       - `num_labels (int)`: Number of labels for the classification task.
-       - `max_length (int)`: Maximum sequence length.
-       - `epochs (int)`: Number of training epochs.
-       - `batch_size (int)`: Batch size for training.
-       - `output_dir (str)`: Directory to save the trained models.
-       - `save_steps (int)`: Number of steps between saving checkpoints.
+
+     - `data_path (str)`: Path to the training data CSV file.
+     - `model_name (str)`: Pre-trained model name from HuggingFace.
+     - `num_labels (int)`: Number of labels for the classification task.
+     - `max_length (int)`: Maximum sequence length.
+     - `epochs (int)`: Number of training epochs.
+     - `batch_size (int)`: Batch size for training.
+     - `output_dir (str)`: Directory to save the trained models.
+     - `save_steps (int)`: Number of steps between saving checkpoints.
 
    - To train the model from the command line, run `fine_tune.py` with the required arguments:
+
      ```bash
      python fine_tune.py \
        --data_path "data/train_data.csv" \
@@ -62,38 +65,44 @@ This folder contains a set of scripts and notebooks designed to process data, tr
 3. **Perform Inference**:
    - Use `inference_demo.ipynb` to perform inferences with your trained model. Specify the model and tokenizer paths (either local or from HuggingFace) and run the notebook cells to see the results.
    - For programmatic inference, you can use the function provided in `inference.py`:
+
      ```python
      from inference import get_inference
      result = get_inference(question="What is the relevance?", paragraph="This is a sample paragraph.", model_path="path/to/model", tokenizer_path="path/to/tokenizer")
      ```
 
-
 ## Repository Contents
 
 ### Python Scripts
 
 1. **`inference.py`**
+
    - This script contains the function to make inferences using the trained model.
    - **Usage**: Import this script and use the provided function to predict the relevance of new data.
    - **Example**:
+
      ```python
      from inference import get_inference
      result = get_inference(question="What is the relevance?", paragraph="This is a sample paragraph.", model_path="path/to/model", tokenizer_path="path/to/tokenizer")
      ```
+
      **Parameters**:
-       - `question (str)`: The question for inference.
-       - `paragraph (str)`: The paragraph to be analyzed.
-       - `model_path (str)`: Path to the pre-trained model.
-       - `tokenizer_path (str)`: Path to the tokenizer of the pre-trained model.
+     - `question (str)`: The question for inference.
+     - `paragraph (str)`: The paragraph to be analyzed.
+     - `model_path (str)`: Path to the pre-trained model.
+     - `tokenizer_path (str)`: Path to the tokenizer of the pre-trained model.
 
 2. **`make_training_data_from_curator.py`**
+
    - This script processes CSV data obtained from a module named `curator` to make it suitable for training the model.
    - **Usage**: Run this script to generate training data from the curator's output and save it in the `Data` folder.
 
 3. **`train_sentence_transformer.py`**
+
    - This script defines a function to train a sentence transformer model, which can be called from other scripts or notebooks.
    - **Usage**: Import and call the `fine_tune_model` function to train your model.
    - **Example**:
+
      ```python
      from train_sentence_transformer import fine_tune_model
      fine_tune_model(
@@ -107,20 +116,22 @@ This folder contains a set of scripts and notebooks designed to process data, tr
          save_steps=500
      )
      ```
+
      **Parameters**:
-       - `data_path (str)`: Path to the training data CSV file.
-       - `model_name (str)`: Pre-trained model name from HuggingFace.
-       - `num_labels (int)`: Number of labels for the classification task.
-       - `max_length (int)`: Maximum sequence length.
-       - `epochs (int)`: Number of training epochs.
-       - `batch_size (int)`: Batch size for training.
-       - `output_dir (str)`: Directory to save the trained models.
-       - `save_steps (int)`: Number of steps between saving checkpoints.
+     - `data_path (str)`: Path to the training data CSV file.
+     - `model_name (str)`: Pre-trained model name from HuggingFace.
+     - `num_labels (int)`: Number of labels for the classification task.
+     - `max_length (int)`: Maximum sequence length.
+     - `epochs (int)`: Number of training epochs.
+     - `batch_size (int)`: Batch size for training.
+     - `output_dir (str)`: Directory to save the trained models.
+     - `save_steps (int)`: Number of steps between saving checkpoints.
 
 4. **`fine_tune.py`**
    - This script allows you to train a sentence transformer model from the command line.
    - **Usage**: Run this script from the command line with the necessary arguments.
    - **Example**:
+
      ```bash
      python fine_tune.py \
        --data_path "data/train_data.csv" \
@@ -136,11 +147,13 @@ This folder contains a set of scripts and notebooks designed to process data, tr
 ### Jupyter Notebooks
 
 1. **`inference_demo.ipynb`**
+
    - A notebook to demonstrate how to perform inferences using a custom model and tokenizer.
    - **Features**: Allows specifying model and tokenizer paths, which can be local paths or HuggingFace paths.
    - **Usage**: Open this notebook and follow the instructions to test inference with your own models.
 
 2. **`make_sample_training_data.ipynb`**
+
    - This notebook was used to create sample training data from a sample CSV file.
    - **Usage**: Open and run this notebook to understand the process of creating sample data for training.
 
@@ -153,34 +166,38 @@ This folder contains a set of scripts and notebooks designed to process data, tr
 - **`Data/`**
   - This folder contains the processed training data obtained from the `curator` module. It serves as the input for training the sentence transformer model.
 
-
 ## Setting Up the Environment
 
 To set up the working environment for this repository, follow these steps:
 
 1. **Clone the repository**:
+
    ```bash
    git clone https://github.com/yourusername/folder-relevance-detector.git
    cd folder-relevance-detector
    ```
 
 2. **Create a new virtual environment and activate it**:
+
    ```bash
    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
    ```
 
 3. **Install PDM**:
+
    ```bash
    pip install pdm
    ```
 
 4. **Sync the environment using PDM**:
+
    ```bash
    pdm sync
    ```
 
 5. **Add any new library**:
+
    ```bash
    pdm add <library-name>
    ```