File size: 1,726 Bytes
1cc50a7 7b6966e 1cc50a7 7b6966e 79117bb 1cc50a7 50504f0 1cc50a7 cd3b461 1cc50a7 dd62a41 1cc50a7 50504f0 1cc50a7 50504f0 1cc50a7 50504f0 1cc50a7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
language:
- zh
- yue
- multilingual
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- x-tech/cantonese-mandarin-translations
base_model: google/mt5-base
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on dataset [x-tech/cantonese-mandarin-translations](https://huggingface.co/datasets/x-tech/cantonese-mandarin-translations).
## Model description
The model translates Mandarin sentences to Cantonese.
## Intended uses & limitations
When you use the model, please make sure to add `translate mandarin to cantonese: <sentence>` (please note the space after colon) before the text you want to translate.
## Training and evaluation data
Training Dataset: [x-tech/cantonese-mandarin-translations](https://huggingface.co/datasets/x-tech/cantonese-mandarin-translations)
## Training procedure
Training is based on [example in transformers library](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
Since we still need to set up validation set, we do not have any training results yet.
### Framework versions
- Transformers 4.12.5
- Pytorch 1.8.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|