Instructions to use FiveC/za_zh_sc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/za_zh_sc with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/za_zh_sc") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/za_zh_sc") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- .gitattributes +1 -0
- README.md +63 -0
- tokenizer.json +3 -0
- tokenizer_config.json +20 -0
.gitattributes
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: transformers
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base_model: facebook/mbart-large-50-many-to-many-mmt
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tags:
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- generated_from_trainer
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metrics:
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- sacrebleu
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model-index:
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- name: za_zh_sc
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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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should probably proofread and complete it, then remove this comment. -->
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# za_zh_sc
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This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.6823
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- Sacrebleu: 6.0325
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Sacrebleu |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|
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| 2.4359 | 1.0 | 309 | 2.9542 | 3.4303 |
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| 1.5757 | 2.0 | 618 | 2.7130 | 4.8701 |
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| 1.2767 | 3.0 | 927 | 2.6823 | 6.0325 |
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.10.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:1eeb5a28135f15f64314077af5cae74547d898c979266144ecd95f709e76b008
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size 16793473
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"extra_special_tokens": [
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"<SEP>"
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],
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"is_local": false,
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"language_codes": "ML50",
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"mask_token": "<mask>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"src_lang": "zh_CN",
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"tgt_lang": null,
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"tokenizer_class": "MBart50Tokenizer",
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"unk_id": 0,
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"unk_token": "<unk>"
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}
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