Instructions to use Shularp/krirk-finetuned-Helsinki-NLP_opus-mt-ar-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shularp/krirk-finetuned-Helsinki-NLP_opus-mt-ar-en 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="Shularp/krirk-finetuned-Helsinki-NLP_opus-mt-ar-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shularp/krirk-finetuned-Helsinki-NLP_opus-mt-ar-en") model = AutoModelForSeq2SeqLM.from_pretrained("Shularp/krirk-finetuned-Helsinki-NLP_opus-mt-ar-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
krirk-finetuned-Helsinki-NLP_opus-mt-ar-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3665
- Bleu: 35.0219
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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|---|---|---|---|
| 1.4469 | 1.0 | 32 | 1.3744 | 34.9616 |
| 1.2938 | 2.0 | 64 | 1.3674 | 34.9145 |
| 1.2582 | 3.0 | 96 | 1.3665 | 35.0219 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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