Instructions to use la-min/translate-en-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use la-min/translate-en-ja 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="la-min/translate-en-ja")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("la-min/translate-en-ja") model = AutoModelForSeq2SeqLM.from_pretrained("la-min/translate-en-ja") - Notebooks
- Google Colab
- Kaggle
translate-en-ja
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-jap on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.4240
- Bleu: 0.2880
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: 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
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for la-min/translate-en-ja
Base model
Helsinki-NLP/opus-mt-en-jap