Instructions to use omaryshchenko/w2v-bert-2.0-polish-CV16.0-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omaryshchenko/w2v-bert-2.0-polish-CV16.0-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="omaryshchenko/w2v-bert-2.0-polish-CV16.0-v3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("omaryshchenko/w2v-bert-2.0-polish-CV16.0-v3") model = AutoModelForCTC.from_pretrained("omaryshchenko/w2v-bert-2.0-polish-CV16.0-v3") - Notebooks
- Google Colab
- Kaggle
w2v-bert-2.0-polish-CV16.0-v3
This model is a fine-tuned version of facebook/w2v-bert-2.0 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- training_steps: 5000
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
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Model tree for omaryshchenko/w2v-bert-2.0-polish-CV16.0-v3
Base model
facebook/w2v-bert-2.0