Instructions to use guymorlan/English2Dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guymorlan/English2Dialect with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("guymorlan/English2Dialect") model = AutoModelForSeq2SeqLM.from_pretrained("guymorlan/English2Dialect") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - Levantine Arabic | |
| - Shami | |
| - English | |
| - Egyptian | |
| # English2Dialect | |
| This model generates translations from English to colloquial Arabic, conditioning the translations on dialect. Supported dialects are: Palestinian/Jordanian, Syrian, Lebanese and Egyptian. | |
| Dialect is specified via the first input token which should be either P/S/L/E. | |
| For example, to translate to Palestinian/Jordanian, use the input: | |
| `P What time is it now?` | |
| And for Syrian: | |
| `S What time is it now?` | |
| - **Demo:** https://huggingface.co/spaces/guymorlan/English2Shami | |
| - **Version w/o conditional generation (Levantine only):** https://huggingface.co/guymorlan/English2Shami | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| ## Training Data | |
| The model was trained by fine-tuning the opus-mt-ar-en (MSA to English) model on ~85K parallel sentences in four dialects of colloquial Arabic. | |
| ## Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** Guy Mor-Lan (guy.mor@mail.huji.ac.il) | |
| - **Model type:** MarianMT Seq2Seq | |
| - **License:** MIT | |
| - **Finetuned from model:** Helsinki-NLP/opus-mt-ar-en[https://huggingface.co/Helsinki-NLP/opus-mt-ar-en] | |
| <!--- **Repository:** [More Information Needed] --> |