hebrew-nlp-toolkit

name	hebrew-nlp-toolkit
description	Guide developers in using Hebrew NLP models and tools including DictaLM, DictaBERT, AlephBERT, and ivrit.ai. Use when user asks about Hebrew text processing, Hebrew NLP, "ivrit", Hebrew tokenization, Hebrew NER, Hebrew sentiment analysis, Hebrew speech-to-text, or needs to process Hebrew language text programmatically. Covers model selection, preprocessing, and Hebrew-specific NLP challenges. Do NOT use for Arabic NLP (different tools) or general English NLP tasks. Activate for: עיבוד שפה טבעית, עיבוד טקסט בעברית, טוקניזציה, זיהוי ישויות, ניתוח רגשות, המרת דיבור לטקסט, ניתוח מורפולוגי, מודל שפה עברי.
license	MIT
allowed-tools	Bash(python:*) Bash(pip:*)
compatibility	Requires Python and transformers library for model usage. GPU recommended for large models.

Hebrew NLP Toolkit

Instructions

Step 1: Identify the NLP Task

Task	Recommended Model	HuggingFace ID	Size	Notes
Text generation (large)	DictaLM 3.0 24B Base	dicta-il/DictaLM-3.0-24B-Base	24B	Best Hebrew generation, built on Mistral-Small-3.1-24B
Text generation (small)	DictaLM 3.0 Nemotron Instruct	dicta-il/DictaLM-3.0-Nemotron-12B-Instruct	12B	Instruction-tuned, smaller footprint
Reasoning / chain-of-thought	DictaLM 3.0 24B Thinking	dicta-il/DictaLM-3.0-24B-Thinking	24B	Emits explicit thinking blocks before answering
Lightweight / edge	DictaLM 3.0 1.7B Thinking (GGUF)	dicta-il/DictaLM-3.0-1.7B-Thinking-GGUF	1.7B	Runs on laptop / CPU via llama.cpp
Classification / fill-mask	DictaBERT	dicta-il/dictabert	184M	Fast, good accuracy
NER	DictaBERT NER	dicta-il/dictabert-ner	184M	Recognizes PER, GPE, TIMEX, TTL
Sentiment	DictaBERT Sentiment	dicta-il/dictabert-sentiment	184M	Hebrew sentiment classification
Morphology	DictaBERT Morph	dicta-il/dictabert-morph	184M	Prefix segmentation and POS
Hebrew QA	DictaBERT HeQ	dicta-il/dictabert-heq	184M	Extractive question answering
Embeddings (modern)	NeoDictaBERT Bilingual Embed	dicta-il/neodictabert-bilingual-embed	400M	Hebrew-English sentence embeddings
Embeddings (legacy)	AlephBERT	onlplab/alephbert-base	110M	Older baseline for similarity
Speech-to-text	ivrit.ai Whisper v3	ivrit-ai/whisper-large-v3	1.55B	Fine-tuned on the ivrit.ai Hebrew speech corpus (a 22K+ hour dataset)
Speech-to-text (fast)	ivrit.ai Whisper v3 Turbo CT2	ivrit-ai/whisper-large-v3-turbo-ct2	809M	CTranslate2, ~3x faster inference

Step 2: Install and Load Model

DictaBERT (base model, fill-mask):

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert")
model = AutoModelForMaskedLM.from_pretrained("dicta-il/dictabert")

dicta-il/dictabert is a masked-LM base with NO classification head. Do not load it with AutoModelForSequenceClassification and run inference, that instantiates a randomly-initialised head and returns meaningless predictions. For classification, either fine-tune it on labeled data first, or use a ready task-specific model such as dicta-il/dictabert-sentiment (sentiment) or dicta-il/dictabert-ner (NER).

DictaLM 3.0 (generation, 12B instruct):

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dicta-il/DictaLM-3.0-Nemotron-12B-Instruct")
model = AutoModelForCausalLM.from_pretrained("dicta-il/DictaLM-3.0-Nemotron-12B-Instruct")

For the strongest quality, swap to dicta-il/DictaLM-3.0-24B-Base. For reasoning tasks (math, multi-step logic), use dicta-il/DictaLM-3.0-24B-Thinking, which writes its chain of thought inside an explicit thinking block before the final answer.

ivrit.ai Whisper (speech-to-text):

from transformers import pipeline
# ivrit.ai fine-tuned Hebrew ASR, based on openai/whisper-large-v3
pipe = pipeline("automatic-speech-recognition", model="ivrit-ai/whisper-large-v3")
result = pipe("audio.wav", generate_kwargs={"language": "he"})

For lower latency, use ivrit-ai/whisper-large-v3-turbo-ct2 via faster-whisper (CTranslate2 backend, roughly 3x faster on GPU).

Step 3: Hebrew Text Preprocessing

Before feeding text to models:

1. Normalize: Remove extra whitespace, normalize Unicode (NFC)
2. Handle niqqud: Remove diacritics unless specifically needed
3. Handle English: Decide whether to keep, translate, or mark English tokens
4. Tokenization: Hebrew tokenizers handle morphological splitting

import re
import unicodedata

def preprocess_hebrew(text):
    # Normalize Unicode
    text = unicodedata.normalize('NFC', text)
    # Remove niqqud (diacritics) - range U+0591 to U+05C7
    text = re.sub(r'[֑-ׇ]', '', text)
    # Normalize whitespace
    text = re.sub(r'\s+', ' ', text).strip()
    return text

Step 4: Nikud Restoration (Diacritization)

The preprocessing above removes nikud. The opposite task, adding nikud back to unvocalized text, is called diacritization and is its own core Hebrew NLP problem (useful for text-to-speech, language learning, liturgical text, and disambiguation).

Dicta's Nakdan is the standard tool. The character-level model is published on HuggingFace:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-large-char-menaked')
model = AutoModel.from_pretrained('dicta-il/dictabert-large-char-menaked', trust_remote_code=True)
model.eval()

# The model exposes a `predict` helper that returns vocalized text
vocalized = model.predict(['שלום עולם'], tokenizer)

Notes:

• trust_remote_code=True is required because the model ships a custom predict head. Review the model card before enabling it.
• Diacritization is ambiguous: the same consonantal text can have multiple valid vocalizations depending on context. Treat output as a best guess, not ground truth.
• For full sentence-level vocalization with morphological context, Dicta's hosted Nakdan (see Step 6) generally outperforms a raw character model.

Step 5: Hosted Dicta REST APIs

If you do not want to run models locally, Dicta exposes its tools (Nakdan, morphology, and more) as hosted web services. This suits low-volume use, prototyping, or environments without a GPU.

• Nakdan (diacritization): https://nakdan.dicta.org.il/
• Developer access and API details: https://dicta.org.il/developers

Verify the current endpoint shape, request format, rate limits, and terms of use on the developer page before integrating, as hosted API contracts change. Do not assume an endpoint URL or payload schema, consult the official developer docs.

Step 6: Alternative NLP Entry Points

Dicta and ivrit.ai are the primary Hebrew-specific stacks, but two general NLP frameworks also ship Hebrew pipelines:

• HebSpacy (https://github.com/8400TheHealthNetwork/HebSpacy): a spaCy pipeline for Hebrew with tokenization, POS, lemmatization, and NER. Good when you already use spaCy and want a single API.
• Stanza (https://stanfordnlp.github.io/stanza/): Stanford's NLP toolkit ships a Hebrew model with tokenization, morphology, lemmatization, and dependency parsing. Good for academic / cross-lingual pipelines.

For best Hebrew accuracy, Dicta models still lead. Use these frameworks when integration convenience outweighs raw quality.

Step 7: Handle Hebrew-Specific Challenges

• Morphological analysis: Use Dicta morphological analyzer for accurate word segmentation
• No capital letters: Hebrew has no upper/lowercase distinction -- NER is harder
• Right-to-left in code: Ensure proper bidi handling in string operations
• Mixed Hebrew-English: Common in tech text, may need separate processing

Examples

Example 1: Hebrew Text Classification

User says: "I need to classify Hebrew customer reviews as positive or negative" Result: Guide to use DictaBERT-Sentiment with fine-tuning on domain data.

Example 2: Hebrew Named Entity Recognition

User says: "Extract company and person names from Hebrew articles" Result: Use DictaBERT-NER model, demonstrate with example text.

Bundled Resources

Scripts

• scripts/preprocess_hebrew.py: Normalize Hebrew text before feeding it to NLP models (DictaBERT, DictaLM, AlephBERT). Handles Unicode NFC normalization, niqqud removal, whitespace cleanup, URL stripping, shekel symbol normalization, and mixed Hebrew-English text segmentation. Run: python scripts/preprocess_hebrew.py --help

References

• references/model-comparison.md: Side-by-side comparison of Hebrew NLP models (DictaLM 3.0, DictaBERT, AlephBERT, NeoDictaBERT, ivrit.ai Whisper) with VRAM requirements, HuggingFace IDs, and a task-to-model mapping table. Consult when choosing which model to use for a specific Hebrew NLP task.

Reference Links

Source	URL	What to Check
DICTA Israel Center for Text Analysis (HuggingFace)	https://huggingface.co/dicta-il	Latest DictaLM and DictaBERT model variants, IDs, release notes
ivrit.ai (HuggingFace)	https://huggingface.co/ivrit-ai	Current Whisper fine-tunes for Hebrew ASR, dataset versions
AlephBERT on HuggingFace	https://huggingface.co/onlplab/alephbert-base	AlephBERT model card and usage
ivrit.ai project site	https://www.ivrit.ai/en/	Dataset size, license, research papers
NNLP-IL (Israeli NLP community)	https://github.com/NNLP-IL	Curated list of Hebrew NLP resources and benchmarks
DictaLM 3.0 Technical Report (Dicta)	https://dicta.org.il/publications/DictaLM_3_0___Techincal_Report.pdf	Architecture, base-model lineage, training data, eval results
Dicta Nakdan (diacritization)	https://nakdan.dicta.org.il/	Hosted nikud restoration tool
Dicta developer access	https://dicta.org.il/developers	Hosted REST API details and terms
HebSpacy (spaCy for Hebrew)	https://github.com/8400TheHealthNetwork/HebSpacy	spaCy Hebrew pipeline, install instructions
Stanza (Hebrew model)	https://stanfordnlp.github.io/stanza/	Hebrew tokenization, morphology, parsing

Gotchas

• Hebrew has no capital letters, so agents cannot use capitalization-based NER (Named Entity Recognition) heuristics that work for English. Hebrew NER requires morphological analysis or trained models.
• Hebrew words can be prefixed with multiple particles (prepositions, conjunctions, articles) that are written as part of the word. The string "ובבית" (u-va-bayit) is "and in the house" as a single token. Agents may treat it as one word.
• The Hebrew letter system has five final forms (sofit): kaf, mem, nun, pe, tsadi. Agents may normalize these to their non-final forms, breaking word matching and search.
• Nikud (vowel diacritics) is almost never present in modern Hebrew text. Agents trained on nikud-annotated text may fail on standard unvocalized Hebrew. Always design for nikud-less input.

Troubleshooting

Error: "Tokenization produces unexpected results"

Cause: Hebrew morphology splitting prefixes (b-, k-, l-, m-, sh-, v-) Solution: This is expected behavior. Hebrew words like "bveit" (in the house) are split into morphemes.

Error: "GPU out of memory"

Cause: DictaLM 3.0 24B needs roughly 48GB VRAM in BF16, and the Nemotron-12B variant needs roughly 24GB. Solution: Drop to dicta-il/DictaLM-3.0-Nemotron-12B-Instruct (12B), the 1.7B Thinking GGUF variant, or use the FP8 / W4A16 quantized checkpoints published under the same org (e.g., DictaLM-3.0-24B-Base-FP8). For laptop-class hardware, run the 1.7B GGUF via llama.cpp.