Instructions to use nour-world/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nour-world/test-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("nour-world/test-model") model = AutoModelForAudioFrameClassification.from_pretrained("nour-world/test-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from nour-world/test-model: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
-
https://huggingface.co/nour-world/test-model/resolve/main/README.md
- Command line
-
hf download hf://nour-world/test-model/README.md
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curl -L -o README.md https://huggingface.co/nour-world/test-model/resolve/main/README.md
1.15 kB
metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
model-index:
- name: test-model
results: []
test-model
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: 0.0003
- train_batch_size: 49
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Framework versions
- Transformers 4.51.3
- Pytorch 2.2.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1