Update Readme

README.md

@@ -7,11 +7,6 @@ tags:
 - convnext-audio
 - audioset
 inference: false
-extra_gated_prompt: "The collected information will help acquire a better knowledge of who is using our audio event tools. If relevant, please cite our Interspeech 2023 paper (Bibtex below)."
-extra_gated_fields:
-  Company/university: text
-  Website: text
-  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 [(Paper)](https://www.isca-speech.org/archive/interspeech_2023/pellegrini23_interspeech.html).
@@ -36,10 +31,6 @@ 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
@@ -48,7 +39,7 @@ import torchaudio
 from audioset_convnext_inf.pytorch.convnext import ConvNeXt
 from audioset_convnext_inf.utils.utilities import read_audioset_label_tags
 
-model = ConvNeXt.from_pretrained("topel/ConvNeXt-Tiny-AT", use_auth_token="ACCESS_TOKEN_GOES_HERE", map_location='cpu')
+model = ConvNeXt.from_pretrained("topel/ConvNeXt-Tiny-AT", map_location='cpu')
 
 print(
     "# params:",