Add AutoTrain backend API details

.env.example

@@ -0,0 +1,4 @@
+AUTOTRAIN_USERNAME=autoevaluator # The bot that authors evaluation jobs
+HF_TOKEN=hf_xxx # An API token of the `autoevaluator` user
+AUTOTRAIN_BACKEND_API=https://api-staging.autotrain.huggingface.co # The AutoTrain backend to send jobs to. Use https://api.autotrain.huggingface.co for prod
+DATASETS_PREVIEW_API=https://datasets-server.huggingface.co # The API to grab dataset information from

README.md

@@ -22,7 +22,7 @@ The table below shows which tasks are currently supported for evaluation in the
 | `multi_class_classification`       |     ✅     |
 | `multi_label_classification`       |     ❌     |
 | `entity_extraction`                |     ✅     |
-| `extractive_question_answering`    |     ❌     |
+| `extractive_question_answering`    |     ✅     |
 | `translation`                      |     ✅     |
 | `summarization`                    |     ✅     |
 | `image_binary_classification`      |     ✅     |
@@ -30,14 +30,28 @@ The table below shows which tasks are currently supported for evaluation in the
 
 ## Installation
 
-To run the application, first clone this repository and install the dependencies as follows:
+To run the application locally, first clone this repository and install the dependencies as follows:
 
 ```
 pip install -r requirements.txt
 ```
 
-Then spin up the application by running:
+Next, copy the example file of environment variables:
+
+```
+cp .env.examples .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.autotrain.huggingface.co
 ```