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
## 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")
```
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"description": "Labelator.io - python client\n==========================\n\nPython client for **[Labelator.io](https://www.Labelator.io)** - labeling and ML training studio\n\n\n\n## Install\n```\npip install labelatorio\n```\n\n## Getting your API token\n![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\")\n\n\n## Docs:\nFull Labelator.io documentation can be found here [docs.labelator.io](https://docs.labelator.io)\n\nPage dedicated this client can be found [here](https://docs.labelator.io/docs/integrations/python_sdk)\n\n## Usage\n\n\n### Connecting client\n\n``` python\n import labelatorio\n client = labelatorio.Client(api_token=\"your_api_token\")\n\n```\n\n### Getting project info\n\nPackage requirements are handled using pip. To install them do\n\n```python\n# get project by id\nexisting_project = client.projects.get(\"2fab1778-e8b1-4327-ac83-16dd0e783ab4\")\n\n# if you have just name\nexisting_project = client.projects.get_by_name(\"my name\")\n\n# or if you don't know the exact name\nexisting_project = client.projects.search(\"my name\")\n```\n\n### Adding, updating documents\n\n```python\ndf = pd.DataFrame({\n \"key\":[\"first\",\"second\"], # mandatory\n \"text\":[\"this is my first text\", \"completely different text...\"], # mandatory\n \"my_custom_column\":[\"note 1\",None] # optional\n \"labels\":[[\"ClassA\"],None] #optional if you have labels - should be defined in project\n })\n\nids = client.documents.add_documents(project_id, data=df)\n\nclient.documents.set_labels(project_id,ids[1],[\"ClassB\"])\n```\n\n### Quering documents\n\n```python\n# simple keyword search ... \nfound = client.documents.search(keyword=\"completely different\") \n\n# find all documents where \"ClassA\" was predicted\nfound = client.documents.search( predicted_label=\"ClassA\")\n\n# find all documents where \"ClassA\" was incorrectly predicted\nfound = client.documents.search( false_positives=\"ClassA\")\n\n```\n",
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