Update readme

README.md

@@ -14,13 +14,14 @@ extra_gated_fields:
   I plan to use this model for (task, type of audio data, etc): text
 ---
 
-**ConvNeXt-Tiny-AT** is an audio tagging CNN model, trained on **AudioSet** (balanced+unbalanced subsets). It reached 0.471 mAP on the test set.
+**ConvNeXt-Tiny-AT** is an audio tagging CNN model, trained on **AudioSet** (balanced+unbalanced subsets). It reached 0.471 mAP on the test set [(Paper)](https://www.isca-speech.org/archive/interspeech_2023/pellegrini23_interspeech.html).
 
 The model expects as input audio files of duration 10 seconds, and sample rate 32kHz.
 It provides logits and probabilities for the 527 audio event tags of AudioSet (see http://research.google.com/audioset/index.html).
 Two methods can also be used to get scene embeddings (a single vector per file) and frame-level embeddings, see below.
 The scene embedding is obtained from the frame-level embeddings, on which mean pooling is applied onto the frequency dim, followed by mean pooling + max pooling onto the time dim.
 
+
 # Install
 
 This code is based on our repo: https://github.com/topel/audioset-convnext-inf
@@ -35,6 +36,10 @@ pip install git+https://github.com/topel/audioset-convnext-inf@pip-install
 Below is an example of how to instantiate our model convnext_tiny_471mAP.pth
 
 ```python
+# 1. visit hf.co/topel/ConvNeXt-Tiny-AT and accept user conditions
+# 2. visit hf.co/settings/tokens to create an access token
+# 3. instantiate pretrained model
+
 import os
 import numpy as np
 import torch
@@ -146,6 +151,8 @@ The second model is useful to perform audio captioning on the AudioCaps dataset
 
 # Citation
 
+[Paper available](https://www.isca-speech.org/archive/interspeech_2023/pellegrini23_interspeech.html)
+
 Cite as: Pellegrini, T., Khalfaoui-Hassani, I., Labbé, E., Masquelier, T. (2023) Adapting a ConvNeXt Model to Audio Classification on AudioSet. Proc. INTERSPEECH 2023, 4169-4173, doi: 10.21437/Interspeech.2023-1564
 
 ```bibtex