Instructions to use hf-internal-testing/processor_with_lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/processor_with_lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/processor_with_lm")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm") model = AutoModelForCTC.from_pretrained("hf-internal-testing/processor_with_lm") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 48744e9
fit vocab_size
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
config.json
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"torch_dtype": "float32",
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"transformers_version": "4.16.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size":
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"xvector_output_dim": 512
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}
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"torch_dtype": "float32",
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"transformers_version": "4.16.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 16,
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"xvector_output_dim": 512
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}
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pytorch_model.bin
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:3423d4b48d52919581eb5e711a77b8e358ee3700181be150fb3f6a6c227d35a4
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size 140134
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