Instructions to use skypro1111/mbart-large-50-verbalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skypro1111/mbart-large-50-verbalization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("skypro1111/mbart-large-50-verbalization") model = AutoModelForSeq2SeqLM.from_pretrained("skypro1111/mbart-large-50-verbalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -130,9 +130,8 @@ from huggingface_hub import hf_hub_download
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model_name = "skypro1111/mbart-large-50-verbalization"
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def download_model_from_hf(repo_id=model_name, model_dir="
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"""Download ONNX models from HuggingFace Hub."""
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os.makedirs(model_dir, exist_ok=True)
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files = ["onnx/encoder_model.onnx", "onnx/decoder_model.onnx", "onnx/decoder_model.onnx_data"]
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@@ -140,7 +139,7 @@ def download_model_from_hf(repo_id=model_name, model_dir="onnx"):
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hf_hub_download(
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repo_id=repo_id,
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filename=file,
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local_dir=model_dir
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)
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return files
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model_name = "skypro1111/mbart-large-50-verbalization"
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def download_model_from_hf(repo_id=model_name, model_dir="./"):
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"""Download ONNX models from HuggingFace Hub."""
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files = ["onnx/encoder_model.onnx", "onnx/decoder_model.onnx", "onnx/decoder_model.onnx_data"]
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hf_hub_download(
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repo_id=repo_id,
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filename=file,
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local_dir=model_dir,
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)
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return files
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