72 lines
2.2 KiB
Markdown
72 lines
2.2 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- 24-mohamedyehia/gloss2text-Ar-sft
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language:
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- ar
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base_model:
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- google/gemma-3-270m-it
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pipeline_tag: text-generation
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tags:
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- sign-language
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- arabic
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- ArSL
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- gloss-to-text
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- gemma-3
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- accessibility
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---
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# Gloss2Text-V1-Gemma3-270M (Merged)
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## Overview
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**Gloss2Text-V1-Gemma3-270M** is a fine-tuned version of Google's **Gemma-3-270m-it**, optimized for **Arabic Sign Language (ArSL) gloss-to-text translation**.
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This release is **merged**, which means the LoRA adapters have already been fused into the base model weights for faster inference and easier plug-and-play usage.
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## Model Description
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- **Task:** Converts a sequence of Arabic glosses into a natural, grammatically correct Modern Standard Arabic (MSA) sentence.
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- **Input format:** Arabic gloss text, for example: "أنا شرب ماء الآن"
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- **Output format:** Clean MSA text, for example: "أنا أشرب الماء الآن."
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- **Architecture:** Gemma-3, 270M parameters
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- **Training method:** Supervised fine-tuning with LoRA (rank 64), then merged
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## Training Highlights
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The model was trained on a specialized dataset containing diverse ArSL gloss-sentence pairs.
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- **Final eval loss:** ~0.34
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- **Precision:** Trained with `bf16` for improved numerical stability
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## How to Use
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Because this is a merged model, you can load it directly with the `transformers` library without needing `peft`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "24-mohamedyehia/Gloss2Text-V1-Gemma3-270M"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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def translate_gloss(gloss_text):
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prompt = f"Translate ArSL gloss to an MSA sentence.\nGloss: {gloss_text}\nOutput: "
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=50)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Example
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print(translate_gloss("أنا ذهاب صيدلية"))
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```
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## Developer
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Developed by **Mohamed Yehia**.
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- **LinkedIn:** [Mohamed Yehia](https://www.linkedin.com/in/24-mohamed-yehia/)
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