Instructions to use Achitha/small_data_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Achitha/small_data_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Achitha/small_data_test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Achitha/small_data_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("Achitha/small_data_test") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- simple_tamil
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model-index:
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- name: small_data_test
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results: []
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# small_data_test
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on
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It achieves the following results on the evaluation set:
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- eval_loss: 0.0004
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- eval_wer: 0.0
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- eval_runtime: 11.2151
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- eval_samples_per_second: 2.586
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- eval_steps_per_second: 0.178
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- epoch: 499.0
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- step: 500
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## Model description
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: small_data_test
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results: []
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# small_data_test
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset.
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## Model description
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