Instructions to use aixin2024/finetuned-translation-zh-to-en_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aixin2024/finetuned-translation-zh-to-en_v2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="aixin2024/finetuned-translation-zh-to-en_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("aixin2024/finetuned-translation-zh-to-en_v2") model = AutoModelForMultimodalLM.from_pretrained("aixin2024/finetuned-translation-zh-to-en_v2") - Notebooks
- Google Colab
- Kaggle
finetuned-translation-zh-to-en_v2
This model is a fine-tuned version of Helsinki-NLP/opus-mt-zh-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.6760
- Model Preparation Time: 0.007
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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
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Model tree for aixin2024/finetuned-translation-zh-to-en_v2
Base model
Helsinki-NLP/opus-mt-zh-en