Instructions to use Dandan0K/xls_1b_decoding_fr_decoding_test_iter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dandan0K/xls_1b_decoding_fr_decoding_test_iter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dandan0K/xls_1b_decoding_fr_decoding_test_iter")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Dandan0K/xls_1b_decoding_fr_decoding_test_iter") model = AutoModelForCTC.from_pretrained("Dandan0K/xls_1b_decoding_fr_decoding_test_iter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Dandan0K/xls_1b_decoding_fr_decoding_test_iter: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
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https://huggingface.co/Dandan0K/xls_1b_decoding_fr_decoding_test_iter/resolve/main/README.md
- Command line
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hf download hf://Dandan0K/xls_1b_decoding_fr_decoding_test_iter/README.md
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curl -L -o README.md https://huggingface.co/Dandan0K/xls_1b_decoding_fr_decoding_test_iter/resolve/main/README.md
1.38 kB
| license: apache-2.0 | |
| base_model: jonatasgrosman/wav2vec2-xls-r-1b-french | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: xls_1b_decoding_fr_decoding_test_iter | |
| results: [] | |
| <!-- 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. --> | |
| # xls_1b_decoding_fr_decoding_test_iter | |
| This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-french](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-french) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7122 | |
| - Wer: 0.4249 | |
| ## 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: 1e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 8 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 40 | |
| - num_epochs: 30 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 4.43.0.dev0 | |
| - Pytorch 2.3.1+cu118 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |