Instructions to use Klarly/multilingual-MT_Medical-Diagnostics_ROM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Klarly/multilingual-MT_Medical-Diagnostics_ROM 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="Klarly/multilingual-MT_Medical-Diagnostics_ROM")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Klarly/multilingual-MT_Medical-Diagnostics_ROM") model = AutoModelForSeq2SeqLM.from_pretrained("Klarly/multilingual-MT_Medical-Diagnostics_ROM") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-roa on a dataset of medical diagnostic technical content.
Model Description
- Developed by: Chiara Baffelli
- Language(s) (NLP): EN, FR, ES, IT, PT, RO
- Finetuned from model [optional]: Helsinki-NLP/opus-mt-en-roa
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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