185 lines
5.9 KiB
Markdown
185 lines
5.9 KiB
Markdown
---
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base_model: dicta-il/dictalm2.0-instruct
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library_name: transformers
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license: mit
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language:
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- he
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tags:
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- qasem
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- hebrew
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- causal-lm
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- semantic-parsing
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datasets:
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- biu-nlp/Multilingual_QASem_Datasets
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pipeline_tag: text-generation
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---
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# QASem Hebrew Full Model (DictaLM 2.0)
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This model performs **QA-based semantic parsing (QASem) in Hebrew**.
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## Overview
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This repository provides a **fully fine-tuned model** for performing **QA-based semantic parsing (QASem) in Hebrew**.
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QASem represents predicate–argument structure using **natural-language question–answer pairs**, rather than predefined semantic role labels. This makes the representation more interpretable and flexible across languages.
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The model is based on:
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**Base model:** `dicta-il/dictalm2.0-instruct`
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and was fully fine-tuned for QA-based semantic parsing.
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## ✨ Why this model matters
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Traditional semantic role labeling methods rely on fixed label schemas and costly expert annotation.
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This model takes a different approach by:
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- Representing semantics using **natural-language question–answer pairs**
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- Enabling **automatic dataset construction** via cross-lingual projection
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- Supporting **scalable semantic parsing across languages**
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- Achieving strong performance with **efficient fine-tuned models**
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This makes it possible to build semantic parsers for new languages with minimal cost.
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## Use Cases
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This model can be used for:
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- Research in **QA-based semantic parsing (QASem)** and semantic representation learning
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- Extraction of **predicate–argument structures** from Hebrew text
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- Automatic **dataset creation** for training semantic models in new languages
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- Downstream NLP applications such as:
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- Information extraction
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- Text understanding
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- Factuality and attribution evaluation
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## Language
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- Hebrew 🇮🇱
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## Training Data
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The model was trained on the **Multilingual QASem Dataset**:
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👉 https://huggingface.co/datasets/biu-nlp/Multilingual_QASem_Datasets
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The dataset includes:
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- Automatically generated QASem annotations
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- Train / Development / Test splits
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- Multiple languages: **French, Hebrew, Russian**
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- Tens of thousands of QA pairs per language
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The data was constructed using a cross-lingual projection approach, ensuring scalability across languages.
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## 📄 Associated Work
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This model and the underlying dataset are introduced in:
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[Effective QA-Driven Annotation of Predicate-Argument Relations Across Languages](https://aclanthology.org/2026.eacl-long.112/).
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The paper presents the full methodology, dataset construction process, and evaluation across multiple languages.
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## 🚀 Quick Start (Recommended)
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### Using the XQASem Parser
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For a simple and structured interface, you can use the XQASem parser.
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### Installation
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```bash
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pip install xqasem
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```
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### Basic Example
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```python
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from xqasem import XQasemParser
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parser = XQasemParser.from_language("he")
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sentences = [
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"המומחים הדגישו שהאלגוריתם החדש מאיץ משמעותית את עיבוד הבקשות המורכבות."
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]
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df = parser(sentences)
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print(df)
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```
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## Output Format
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The model produces structured predicate–argument representations in the form of:
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- A predicate (verb or nominal)
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- A natural-language question
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- A corresponding answer span from the sentence
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This structure can be easily converted into tabular or JSON format for downstream use.
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### Example Output
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| sentence | predicate | predicate_type | question | answer |
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| --- | --- | --- | --- | --- |
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| המומחים הדגישו שהאלגוריתם החדש מאיץ משמעותית את עיבוד הבקשות המורכבות. | הדגישו | verb | מי הדגיש משהו? | המומחים |
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| המומחים הדגישו שהאלגוריתם החדש מאיץ משמעותית את עיבוד הבקשות המורכבות. | הדגישו | verb | מה מישהו הדגיש? | שהאלגוריתם החדש מאיץ משמעותית את עיבוד הבקשות המורכבות |
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| המומחים הדגישו שהאלגוריתם החדש מאיץ משמעותית את עיבוד הבקשות המורכבות. | מאיץ | verb | מה מאיץ משהו? | האלגוריתם החדש |
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| המומחים הדגישו שהאלגוריתם החדש מאיץ משמעותית את עיבוד הבקשות המורכבות. | מאיץ | verb | מה משהו מאיץ? | את עיבוד הבקשות המורכבות |
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👉 For more details and advanced usage, see the project repository:
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https://github.com/JohnnieDavidov/xqasem
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## Manual Model Loading (Advanced)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "YonatanDavidov/qasem-he-dictalm2-full"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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```
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## Limitations
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- Performance may degrade on out-of-domain text
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- Complex or ambiguous predicates may lead to inconsistent outputs
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- The model is optimized for QASem-style generation and not for general-purpose text generation
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## 📄 Citation
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If you use this model, please cite our work:
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```
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@inproceedings{davidov-etal-2026-effective,
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title = "Effective {QA}-Driven Annotation of Predicate{--}Argument Relations Across Languages",
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author = "Davidov, Jonathan and
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Slobodkin, Aviv and
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Klein, Shmuel Tomi and
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Tsarfaty, Reut and
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Dagan, Ido and
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Klein, Ayal",
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editor = "Demberg, Vera and
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Inui, Kentaro and
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Marquez, Llu{\'i}s",
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booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
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month = mar,
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year = "2026",
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address = "Rabat, Morocco",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2026.eacl-long.112/",
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doi = "10.18653/v1/2026.eacl-long.112",
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pages = "2484--2502",
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ISBN = "979-8-89176-380-7",
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}
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```
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