semvec/pypi-packages huggingface dataset:
onnxruntime-azure 1.15.0
ONNX Runtime is a runtime accelerator for Machine Learning models
License: MIT License
Keywords: onnx machine learning

ONNX Runtime
============

ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please see `aka.ms/onnxruntime <https://aka.ms/onnxruntime/>`_ or the `Github project <https://github.com/microsoft/onnxruntime/>`_.


Changes
-------

1.15.0
^^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.15.0

1.14.0
^^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.14.0

1.13.0
^^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.13.0

1.12.0
^^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.12.0

1.11.0
^^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.11.0

1.10.0
^^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.10.0

1.9.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.9.0

1.8.2
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.2

1.8.1
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.1

1.8.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.0

1.7.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.0

1.6.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.0

1.5.3
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.3

1.5.2
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.2

1.5.1
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.1


1.4.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.0

1.3.1
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.1

1.3.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.0

1.2.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.0

1.1.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.0

1.0.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.0

0.5.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.0

0.4.0
^^^^^

Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
labelatorio 0.4.1
Labelator.io python client
License: MIT License
Requires Python: >=3.8
Keywords: client labelator-io

Labelator.io - python client
==========================

Python client for **[Labelator.io](https://www.Labelator.io)** - labeling and ML training studio



## Install
```
pip install labelatorio
```

## Getting your API token
![Click on "user" icon in the right-top corner, select "User settings". Click on Get API token. Copy token into clipboard. ](/docs/get_token.png "Get your api token")


## Docs:
Full Labelator.io documentation can be found here [docs.labelator.io](https://docs.labelator.io)

Page dedicated this client can be found [here](https://docs.labelator.io/docs/integrations/python_sdk)

## Usage


### Connecting client

``` python
    import labelatorio
    client = labelatorio.Client(api_token="your_api_token")

```

### Getting project info

Package requirements are handled using pip. To install them do

```python
# get project by id
existing_project = client.projects.get("2fab1778-e8b1-4327-ac83-16dd0e783ab4")

# if you have just name
existing_project = client.projects.get_by_name("my name")

# or if you don't know the exact name
existing_project = client.projects.search("my name")
```

### Adding, updating documents

```python
df = pd.DataFrame({
        "key":["first","second"], # mandatory
        "text":["this is my first text", "completely different text..."],  # mandatory
        "my_custom_column":["note 1",None] # optional
        "labels":[["ClassA"],None] #optional if you have labels - should be defined in project
    })

ids = client.documents.add_documents(project_id, data=df)

client.documents.set_labels(project_id,ids[1],["ClassB"])
```

### Quering documents

```python
# simple keyword search ... 
found = client.documents.search(keyword="completely different") 

# find all documents where "ClassA" was predicted
found = client.documents.search( predicted_label="ClassA")

# find all documents where "ClassA" was incorrectly predicted
found = client.documents.search( false_positives="ClassA")

```