mazkooleg/digit_mask_augmented_raw
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How to use mazkooleg/digit-mask-data2vec-audio-base-960h-ft with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="mazkooleg/digit-mask-data2vec-audio-base-960h-ft") # Load model directly
from transformers import AutoTokenizer, AutoModelForAudioClassification
tokenizer = AutoTokenizer.from_pretrained("mazkooleg/digit-mask-data2vec-audio-base-960h-ft")
model = AutoModelForAudioClassification.from_pretrained("mazkooleg/digit-mask-data2vec-audio-base-960h-ft")This model is a fine-tuned version of facebook/data2vec-audio-base-960h on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss |
|---|---|---|---|---|---|
| 0.0167 | 1.0 | 14264 | 0.9975 | 0.9975 | 0.0108 |
| 0.0016 | 2.0 | 28528 | 0.9991 | 0.9991 | 0.0067 |
| 0.0063 | 3.0 | 42792 | 0.9987 | 0.9987 | 0.0078 |