Instructions to use eunyounglee/mBART_tokenizer_custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyounglee/mBART_tokenizer_custom with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eunyounglee/mBART_tokenizer_custom") model = AutoModelForSeq2SeqLM.from_pretrained("eunyounglee/mBART_tokenizer_custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from eunyounglee/mBART_tokenizer_custom: direct link, hf CLI and curl.
- Browser
- Download file 1.61 kB
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https://huggingface.co/eunyounglee/mBART_tokenizer_custom/resolve/main/README.md
- Command line
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hf download hf://eunyounglee/mBART_tokenizer_custom/README.md
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curl -L -o README.md https://huggingface.co/eunyounglee/mBART_tokenizer_custom/resolve/main/README.md
1.61 kB
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: mBART_tokenizer_custom | |
| 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. --> | |
| # mBART_tokenizer_custom | |
| This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the 한국어 방언 발화(경상도) dataset from AI Hub. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0045 | |
| - Bleu: 78.9009 | |
| - Gen Len: 20.5578 | |
| ## 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: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | |
| | 0.048 | 1.0 | 12500 | 0.0259 | 93.2721 | 22.2035 | | |
| | 0.0251 | 2.0 | 25000 | 0.0106 | 87.1169 | 21.7826 | | |
| | 0.0105 | 3.0 | 37500 | 0.0045 | 78.9009 | 20.5578 | | |
| ### Framework versions | |
| - Transformers 4.22.2 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.5.1 | |
| - Tokenizers 0.12.1 | |