text2mapdata


Nametext2mapdata JSON
Version 0.1.1 PyPI version JSON
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home_pagehttps://github.com/papasega/text2embeddingview
SummaryA python package to map your own csv files data using Atlas from NOMIC
upload_time2023-04-13 04:33:11
maintainer
docs_urlNone
authorPapa Séga WADE
requires_python
licenseMIT
keywords embedding visualization map text csv search keywords dynamic
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            This is a vesy simple way to map your text data using [Altas from NOMIC](https://docs.nomic.ai/index.html) using the lib `click`. 

You have to create an account to get API_KEY NOMIC. 

Atlas enables you to:

Store, update and organize multi-million point datasets of unstructured text, images and embeddings.

Visually interact with your datasets from a web browser.

Run semantic search and vector operations over your datasets.

Use Atlas to:

    - Visualize, interact, collaborate and share large datasets of text and embeddings.
    
    - Collaboratively clean, tag and label your datasets
    
    - Build high-availability apps powered by semantic search
    
    - Understand and debug the latent space of your AI model trains

# How to use
### Installation

To install the necessary dependencies, run the following command:

```bash
python -m venv mymapenv 
source mymapenv/bin/activate
pip install --upgrade pip 
pip install text2mapdata
```

## Supported Transformer Models from Hugging Face 

This project supports a variety of transformer models, including models from the Hugging Face Model Hub and sentence-transformers. Below are some examples:
    - Hugging Face Model: 'prajjwal1/bert-mini'
    - Hugging Face Model: 'Sahajtomar/french_semantic'  (french version for semantic search embedding) 
    - Sentence-Transformers Model: 'sentence-transformers/all-MiniLM-L6-v2' etc...

Please ensure that the model you choose is compatible with the project requirements and adjust the `--transformer_model_name` option accordingly.

## To map your text/csv  files

```bash
python main.py --transformer-model-name MODEL_NAME --cache_dir CACHE_DIR --batch-size BATCH_SIZE --file-path FILE_PATH
```
Remarque for the CACHE_DIR : you can setup it like ==> 

```bash
export TRANSFORMERS_CACHE=/path_to_your/transformers_cache
```

Give a fidback. 

            

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