# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("rohithugp/mbart-en-to-ne-translator")
model = AutoModelForMultimodalLM.from_pretrained("rohithugp/mbart-en-to-ne-translator")Quick Links
mbart-en-np-seqtoseq-sentence-translation
This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1896
- Bleu: 40.4595
- Gen Len: 10.288
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.0147 | 1.0 | 1250 | 0.9876 | 40.1501 | 9.885 |
| 0.6038 | 2.0 | 2500 | 1.0122 | 40.728 | 10.113 |
| 0.3557 | 3.0 | 3750 | 1.0809 | 35.9297 | 10.844 |
| 0.2071 | 4.0 | 5000 | 1.1502 | 40.4318 | 10.28 |
| 0.1241 | 5.0 | 6250 | 1.1896 | 40.4595 | 10.288 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for rohithugp/mbart-en-to-ne-translator
Base model
facebook/mbart-large-50-many-to-many-mmt
# 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="rohithugp/mbart-en-to-ne-translator")