Created README

README.md

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+---
+license: apache-2.0
+base_model:
+- facebook/wav2vec2-base
+tags:
+- intent-classification
+- slu
+- audio-classification
+metrics:
+- accuracy
+- f1
+model-index:
+- name: wav2vec2-base-unslurp-gold
+  results: []
+datasets:
+- slurp
+language:
+- en
+pipeline_tag: audio-classification
+library_name: transformers
+---
+
+# wav2vec2-base-UNSLURP-gold
+
+This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the UNSLURP dataset (retain set) for the intent classification task.
+SLURP does not provide speaker-independent splits, which are, however, required by Machine Unlearning techniques to be effective. In fact, the identities present in
+the retain, forget, and test sets must be exclusive to successfully apply and evaluate unlearning methods. To address this, we propose new speaker-independent splits. 
+In the following, we refer to the new dataset as SLURP*, or UNSLURP.
+
+It achieves the following results on the test set:
+- Accuracy: 0.825
+- F1: 0.707
+
+## Model description
+
+The base [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
+
+## Task and dataset description
+
+Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. 
+The dataset used here is [(UN)SLURP](https://arxiv.org/abs/2011.13205), where each utterance is tagged with two intent labels: action and scenario.
+
+## Usage examples
+
+You can use the model directly in the following manner:
+```python
+import torch
+import librosa
+from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
+
+## Load an audio file
+audio_array, sr = librosa.load("path_to_audio.wav", sr=16000)
+
+## Load model and feature extractor
+model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/wav2vec2-base-unslurp-gold")
+feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
+
+## Extract features
+inputs = feature_extractor(audio_array.squeeze(), sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt")
+
+## Compute logits
+logits = model(**inputs).logits
+```
+
+## Framework versions
+
+- Datasets 3.2.0
+- Pytorch 2.1.2
+- Tokenizers 0.20.3
+- Transformers 4.45.2
+
+## BibTeX entry and citation info
+
+```bibtex
+@inproceedings{koudounas2025unlearning,
+  title={"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding},
+  author={Koudounas, Alkis and Savelli, Claudio and Giobergia, Flavio and Baralis, Elena},
+  booktitle={Proc. Interspeech 2025}, 
+  year={2025},
+}
+```