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enc-gtsc_distilbert-freezed
Masioki/enc-gtsc_distilbert-freezed
enc-gtsc_distilbert-freezed is a machine learning model from Masioki. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- cross-attention-sentence-classifier
- Access
- Public
- Created
- Jun 4, 2024
- Updated
- Jun 6, 2024
- SHA
- 1587806a
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
enc-gtsc_distilbert-freezed
Ground truth text with ASR encoding residual cross attention multi-label DAC
Model description
ASR encoder: Whisper small encoder
Backbone: DistilBert uncased
Pooling: Self attention
Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween
Training and evaluation data
Trained on ground truth.
Evaluated on ground truth (GT) and normalized Whisper small transcripts (E2E).
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00043
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Library
- transformers
- Type
- cross-attention-sentence-classifier
- Languages
- en
- Created
- Jun 4, 2024
- Updated
- Jun 6, 2024