Instructions to use bmd1905/vietnamese-correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bmd1905/vietnamese-correction with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bmd1905/vietnamese-correction") model = AutoModelForSeq2SeqLM.from_pretrained("bmd1905/vietnamese-correction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model-index:
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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#
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This model is a fine-tuned version of [/
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It achieves the following results on the evaluation set:
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- Loss: 0.2273
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- Cer: 0.0246
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- Transformers 4.26.1
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- Pytorch 1.13.0
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- Datasets 2.1.0
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- Tokenizers 0.13.2
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model-index:
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- name: my-custom-vinai
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results: []
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license: apache-2.0
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pipeline_tag: text2text-generation
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vietnamese-corrector
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This model is a fine-tuned version of [vinai/bartpho-word](vinai/bartpho-word) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2273
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- Cer: 0.0246
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- Transformers 4.26.1
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- Pytorch 1.13.0
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- Datasets 2.1.0
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- Tokenizers 0.13.2
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