Instructions to use eunyounglee/mBART_translator_json_sentence_split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyounglee/mBART_translator_json_sentence_split with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eunyounglee/mBART_translator_json_sentence_split") model = AutoModelForSeq2SeqLM.from_pretrained("eunyounglee/mBART_translator_json_sentence_split", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from eunyounglee/mBART_translator_json_sentence_split: direct link, hf CLI and curl.
- Browser
- Download file 1.73 kB
-
https://huggingface.co/eunyounglee/mBART_translator_json_sentence_split/resolve/main/README.md
- Command line
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hf download hf://eunyounglee/mBART_translator_json_sentence_split/README.md
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curl -L -o README.md https://huggingface.co/eunyounglee/mBART_translator_json_sentence_split/resolve/main/README.md
1.73 kB
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: mBART_translator_json_sentence_split | |
| 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_translator_json_sentence_split | |
| This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0769 | |
| - Bleu: 87.2405 | |
| - Gen Len: 27.425 | |
| ## 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: 1e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | |
| | 2.0011 | 1.0 | 2978 | 0.5458 | 63.8087 | 32.3819 | | |
| | 1.1978 | 2.0 | 5956 | 0.1854 | 76.5291 | 27.6781 | | |
| | 0.9276 | 3.0 | 8934 | 0.1123 | 84.7194 | 27.5773 | | |
| | 0.776 | 4.0 | 11912 | 0.0845 | 87.505 | 27.2845 | | |
| | 0.6889 | 5.0 | 14890 | 0.0769 | 87.2405 | 27.425 | | |
| ### Framework versions | |
| - Transformers 4.23.1 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.6.1 | |
| - Tokenizers 0.13.1 | |