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---
title: Model Evaluator
emoji: 📊
colorFrom: red
colorTo: red
sdk: streamlit
sdk_version: 1.10.0
app_file: app.py
---
# Model Evaluator
> Submit evaluation jobs to AutoTrain from the Hugging Face Hub
## Supported tasks
The table below shows which tasks are currently supported for evaluation in the AutoTrain backend:
| Task | Supported |
|:-----------------------------------|:---------:|
| `binary_classification` | ✅ |
| `multi_class_classification` | ✅ |
| `multi_label_classification` | ❌ |
| `entity_extraction` | ✅ |
| `extractive_question_answering` | ✅ |
| `translation` | ✅ |
| `summarization` | ✅ |
| `image_binary_classification` | ✅ |
| `image_multi_class_classification` | ✅ |
## Installation
To run the application locally, first clone this repository and install the dependencies as follows:
```
pip install -r requirements.txt
```
Next, copy the example file of environment variables:
```
cp .env.template .env
```
and set the `HF_TOKEN` variable with a valid API token from the `autoevaluator` user. Finally, spin up the application by running:
```
streamlit run app.py
```
## AutoTrain configuration details
Models are evaluated by AutoTrain, with the payload sent to the `AUTOTRAIN_BACKEND_API` environment variable. The current configuration for evaluation jobs running on Spaces is:
```
AUTOTRAIN_BACKEND_API=https://api-staging.autotrain.huggingface.co
```
To evaluate models with a _local_ instance of AutoTrain, change the environment to:
```
AUTOTRAIN_BACKEND_API=http://localhost:8000
``` |