# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("gyr66/machine_translation")
model = AutoModel.from_pretrained("gyr66/machine_translation", device_map="auto")Quick Links
eval
This model is a fine-tuned version of facebook/mbart-large-cc25 on IWSLT14 En-Zh dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.8405
- eval_bleu: 3.5173
- eval_gen_len: 21.5826
It achieves the following results on the test set:
- test_loss: 3.8337
- test_bleu: 3.277
- test_gen_len: 21.6287
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 7
- num_epochs: 9
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for gyr66/machine_translation
Base model
facebook/mbart-large-cc25
# 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="gyr66/machine_translation")