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Layout llm

To install LayoutLMv3 and train it using a custom dataset stored in a RAM folder on Termux, you'll need to follow these general steps:

1. Prepare Your Environment

First, make sure you have Termux installed on your device. Then, set up a Python environment within Termux.

2. Install Dependencies

Install necessary dependencies including Python, Git, and any other libraries required for LayoutLMv3 and your custom training data.

pkg install python git

3. Clone LayoutLMv3 Repository

Clone the LayoutLMv3 repository from GitHub.

git clone https://github.com/microsoft/unilm.git
cd unilm/layoutlmv3

4. Install LayoutLMv3

Install LayoutLMv3 using the provided setup script or by installing the required dependencies manually.

pip install -r requirements.txt

5. Prepare Training Data

Prepare your custom training data and store it in the RAM folder.

6. Train LayoutLMv3

Use the provided scripts or write your own training script to train LayoutLMv3 on your custom dataset.

python train.py --data_dir /path/to/your/data --output_dir /path/to/save/model

Replace /path/to/your/data with the path to your training data stored in the RAM folder, and /path/to/save/model with the path where you want to save the trained model.

7. Monitor Training Progress

Monitor the training progress to ensure everything is running smoothly and make adjustments as needed.

8. Evaluate and Test

Once training is complete, evaluate the performance of the trained model using validation data, and test it on unseen data to ensure it performs as expected.

Note:

  • Ensure your RAM folder has enough space to store the training data and any temporary files generated during training.
  • Depending on the size of your training data and available RAM, you may need to adjust the batch size and other training parameters to avoid running out of memory.
  • It's recommended to run training on a device with sufficient resources and cooling capabilities to prevent overheating or performance issues.
  • Monitor the training process closely, especially when training with large datasets, to catch any errors or issues early on.

After cloning the LayoutLMv3 repository and installing the required dependencies from the requirements.txt file, you can follow these steps to run the code provided:

  1. Prepare Your Environment:
  2. Open a terminal or command prompt.

  3. Navigate to the directory where you cloned the LayoutLMv3 repository.

  4. Run the Provided Code:

  5. Copy the provided Python script (the one using watchdog for folder monitoring) into a Python file, e.g., real_time_sync.py.
  6. Open the Python file in a text editor and make any necessary adjustments to the paths and configurations (e.g., setting the correct paths for the RAM folder and model output directory).

  7. Save the changes to the Python file.

  8. Execute the Script:

  9. In the terminal or command prompt, navigate to the directory containing the Python file (real_time_sync.py).

  10. Run the script by executing the command: 

    python real_time_sync.py

  11. Monitor the Folder:

  12. Once the script is running, it will continuously monitor the RAM folder for changes.

  13. When new files are added or existing files are modified in the RAM folder, the script will automatically update the LayoutLMv3 model and retrain it with the new data.

  14. Stop Execution:

  15. To stop the execution of the script, you can press Ctrl + C in the terminal or command prompt where the script is running.

  16. Adjustments and Modifications:

  17. You may need to make adjustments to the script based on your specific use case, such as handling errors, integrating suggestion generation, or fine-tuning the training process.

By following these steps, you should be able to run the provided code to continuously monitor a RAM folder, update the LayoutLMv3 model in real-time, and train it with new data as it becomes available. Let me know if you have any further questions!

The provided code snippet does not include functionality to read Markdown files directly. However, you can modify the code to include Markdown file parsing and processing if your RAM folder contains Markdown files. Here's how you can do it:

  1. Use a Markdown parsing library like markdown2, mistune, or markdown to convert Markdown files to plain text.
  2. Iterate through each Markdown file in the RAM folder, parse the content, and add it to your dataset.
  3. Update the update_model function to handle Markdown files in addition to text and PDF files.
  4. Integrate Markdown parsing and text extraction into the main loop of the script to process Markdown files as they are added or modified in the RAM folder.

Here's an example of how you can modify the script to include Markdown parsing:

import time
import os
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
from transformers import LayoutLMv2ForTokenClassification, LayoutLMv2TokenizerFast
from transformers import DataCollatorForTokenClassification
from transformers import Trainer, TrainingArguments
from datasets import load_dataset
import markdown2  # Markdown parsing library

# Define paths
ram_folder_path = "/path/to/ram/folder"
model_output_dir = "/path/to/save/model"

# Initialize LayoutLMv3 tokenizer and model
tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased")
model = LayoutLMv2ForTokenClassification.from_pretrained("microsoft/layoutlmv2-base-uncased")

# Define training arguments
training_args = TrainingArguments(
    output_dir=model_output_dir,
    # Add other training arguments as needed
)

# Define data collator
data_collator = DataCollatorForTokenClassification(tokenizer)

# Define Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
)

# Define event handler for folder monitoring
class FolderMonitor(FileSystemEventHandler):
    def on_any_event(self, event):
        # Event triggered for any change in the folder
        if event.is_directory:
            return
        # Update the model when files are added or modified
        if event.event_type in ['created', 'modified']:
            print("Updating model...")
            update_model()

# Function to update the model with new data from the RAM folder
def update_model():
    # Load data from the RAM folder and update the model
    dataset = load_dataset("text", data_files={"train": ram_folder_path})
    trainer.train(dataset["train"])

# Function for Markdown parsing
def parse_markdown(markdown_path):
    with open(markdown_path, 'r', encoding='utf-8') as markdown_file:
        markdown_content = markdown_file.read()
        html_content = markdown2.markdown(markdown_content)
        # Convert HTML to plain text (strip HTML tags)
        text_content = markdown2.html2text(html_content)
    return text_content

# Function for question and answer interaction
def ask_question():
    question = input("Enter your question: ")
    # Tokenize the question
    inputs = tokenizer(question, return_tensors="pt")
    # Generate answers using the model
    outputs = model(**inputs)
    # Display the answers
    print(outputs)

# Start folder monitoring
observer = Observer()
event_handler = FolderMonitor()
observer.schedule(event_handler, ram_folder_path, recursive=True)
observer.start()

try:
    # Continuously monitor the folder and provide question and answer interactions
    while True:
        # Ask question
        ask_question()
        # Wait for a short interval before asking the next question
        time.sleep(5)
except KeyboardInterrupt:
    observer.stop()

observer.join()

In this modified script: - The parse_markdown function uses markdown2 library to convert Markdown files to plain text. - The update_model function now includes Markdown parsing logic to handle Markdown files in addition to text and PDF files. - When a Markdown file is added or modified in the RAM folder, the script will parse the Markdown content, convert it to plain text, and add it to the dataset before updating the model.