Translation
Transformers
Safetensors
Arabic
t5
text2text-generation
Syrian
Shami
MT
MSA
Dialect
ArabicNLP
text-generation-inference
Instructions to use Omartificial-Intelligence-Space/SHAMI-MT-2MSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Omartificial-Intelligence-Space/SHAMI-MT-2MSA 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="Omartificial-Intelligence-Space/SHAMI-MT-2MSA")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Omartificial-Intelligence-Space/SHAMI-MT-2MSA") model = AutoModelForSeq2SeqLM.from_pretrained("Omartificial-Intelligence-Space/SHAMI-MT-2MSA") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, paper info, usage, and refine description
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for Omartificial-Intelligence-Space/Shami-MT-2MSA by:
- Adding
pipeline_tag: translationto the metadata, which is crucial for model discoverability on the Hugging Face Hub, accurately reflecting its machine translation capabilities. - Correcting a typo in the main model card title ("ton" to "to").
- Including a direct link to the accompanying paper, SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System, at the top of the card.
- Adding the paper's abstract to provide comprehensive context about the model's design, training, and evaluation.
- Clarifying the model description to explicitly state that
SHAMI-MT-2MSAis the Syrian dialect to MSA component of the broaderSHAMI-MTbidirectional system. - Adding a Python sample usage snippet, demonstrating how to load and use the model with the
transformerslibrary, which improves immediate usability. - Refining the citation section to include a proper BibTeX entry for the main paper and removing redundant details.
- Removing the "Model Details" section as its content is either covered by the metadata or the main description.
These improvements make the model card more informative, user-friendly, and aligned with best practices on the Hugging Face Hub.
Omartificial-Intelligence-Space changed pull request status to merged