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Ijambo- banze: Kuva: Icyo ari cyo cyose Umwandiko Na: Kigenga. na Ibigize.
Gukoresha Ijambo- banze:
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Umwandiko, Kohereza A Idosiye, Cyangwa. Aderesi:.
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Our AI processes your request in seconds using the best open-source models.
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, Gukoporora, Cyangwa Gusangiza Igisubizo. ya: Bwite na Koresha.
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curl -X POST https://api.free.ai/v1/chat/ \
-H "Authorization: Bearer sk-free-..." \
-H "Content-Type: application/json" \
-d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Summarize this: ..."}]}'Ijambo- banze: - FAQ
Extracts the most meaningful words and phrases from any long-form text (article, blog post, transcript, product description). Five extraction modes: (1) SEO keywords with search-intent tagging, (2) high-level topics + themes, (3) named entities - people/places/organizations, (4) tag-style lowercase-hyphen-joined tags, (5) academic index terms. Results scored 0-100 by TF-IDF, raw frequency, or semantic relevance. Export as CSV, TXT, or JSON.
Yes - a 1,500-word article extracts in ~350 tokens on the default Qwen 3 30B model, comfortably inside the 6,000 anonymous or 30,000 signed-up daily pool. No sign-up required for your first extraction.
Gukata Gicurasi - Igice Ibyatanzwe ya: Ijambo- banze ($ 99 + / mo). i - Umukandida Ijambo- banze Ibigize na Gushaka Intego.: Koresha iyi Kuri Gushaka Ingingo Ingingo, Hanyuma Komeka / Kuri Kugenzura buri kwezi Gushaka Igice. Na: Kigenga Ijambo- banze -.
: (1) - ( Mburabuzi) Amagambo in Inyandiko in - ya: i. (2) Ububikoshingiro - Ryari: Kuri Reba. (3) ku Kuri i Inyandiko NIBA A Rimwe - ya:.
N - ni A Ikurikiranyanyuguti Bya Amagambo. "Ihindurangero" ni A 2 -. "Imisusire" ni A 3 -. i Kuri 1 - (UMWE Amagambo nka "), 2 - (Umutwe nka "), Cyangwa 3 - (Igihe kirekire - Nka "). 2 + 3 - - UMWE Amagambo.
Yes - the default stoplist removes "the", "and", "of", "is", etc. in 14 languages. You can add custom stopwords via the "Words to exclude" field - useful for suppressing your own brand name, product codes, or boilerplate legalese that would otherwise dominate the results.
Google ku Umukoresha Intego: ("ni"), Igenzura ("Nike Urwego rw'imikorere), Bya ki/ bishaje ("2026"), ("Kugura Air Max Online"). Itagi: Ijambo- banze Na: i Ingingo Rango ya:, Cyangwa - Umuvuduko. ya: Ibikubiyemo Na: Ibiranga.
99 languages via the underlying Qwen 2.5 model. Highest quality on English, Spanish, French, Portuguese, German, Chinese, Japanese. Lower-resource languages work but 3-gram extraction may be less reliable - fall back to 1+2-grams for Arabic, Hindi, Turkish, etc.
The UI runs one text at a time. For bulk extraction wire the /v1/chat/ API into a Python script - iterate over your CMS export, POST each article body, dump the JSON. Bearer auth, 1,000 calls/hour on free accounts, higher limits on pro. Docs at /api/.
RAKE and YAKE are classic lexical algorithms - fast but weak on semantic nuance (they miss "tech startup" if the document says "technology startup" but not "tech" alone). KeyBERT uses BERT embeddings - better semantic quality but slow and requires installation. TextRank is graph-based. Our approach uses a 7B-parameter LLM which does all of the above zero-shot plus search-intent classification - slower per call but zero setup, free, and no ML engineering required.
In frequency and TF-IDF modes, yes - every keyword is derivable from the source text verbatim or with minimal inflection change. In semantic-relevance mode the model may return a controlled-vocabulary version ("pulmonology" even if your text says "lung doctor visit"). The academic-terms mode intentionally prefers controlled vocabulary.
Yes - POST to /v1/chat/ with the same system prompt this page builds (inspect template source for the exact prompt). Returns structured JSON. Good for content-strategy dashboards or CMS plugins. Bearer auth, monthly limits. Docs at /api/.
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