semvec/pypi-packages huggingface dataset:
m-abc-anhnt 1.0.2
Thư viện xử lý kiểm tra quyền theo logic ABAC (Attribute-based access control)
License: MIT
Requires Python: >=3
Keywords: mobio,mobio-engine,m-abac

### ABAC Engine
Thư viện xử lý kiểm tra quyền theo logic ABAC (Attribute-based access control).


### Cài đặt:
```bash
 $ pip3 install m-abac
 ```

### Sử dụng:

##### 3. Save log action account:
   ```python
    from m_abac import PolicyDecisionPoint
    merchant_id = "1b99bdcf-d582-4f49-9715-1b61dfff3924"
    resource = "deal"
    # action = "UpdateFromSale"
    action = "ListFromSale"
    account_id = "704eac91-7416-497f-a17d-d81cfa2d3211"
    user_info = {
        "block": "KHDN",
        "scope_code": "MB##HN"
    }
    request_access = {
        "deal": {
            # "block": "KHCN",
            # "scope_code": "MB##HN##CAU_GIAY"
        }
    }

    pdb = PolicyDecisionPoint(merchant_id=merchant_id, resource=resource, action=action, account_id=account_id,
                              user_info=user_info, request_access=request_access)
    result = pdb.is_allowed()
    print("allow access: {}".format(result.get_allow_access()))
    print("display: {}".format(result.get_display_config()))
    print("filter: {}".format(result.get_filter_config()))
   ```
#### Log - 1.0.0
    - release sdk
text2mapdata 0.1.1
A python package to map your own csv files data using Atlas from NOMIC
License: MIT
Keywords: Embedding,Visualization,Map,Text,CSV,Search keywords,dynamic

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.