Nhazigharị
Ónyénwē n'ọrụ
579 models
Ónweghị ákàrà
Enweghị mkpa ịbanye
Móòdù:
+ GPT-5, Claude, Gemini
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Nke ahụ
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Na-arụ ọrụ n'ihe nchọgharị gị...
Nhazi ngwe n'ụdị na-enweghị AI. Nchọpụta ụda dị mma, na-adịghị mma na nke na-adịghị mma.
Otu esi eji ya Nhazigharị
1
Tinye inu gị
Tinye ngwe, bubata faịlụ, mọọbụ gosi ihe ịchọrọ. Achọrọghị akaụntụ.
2
Pịa iji mepụta
Anyị AI na-enyocha arịrịọ gị n'ime sekọnd site na iji ihe kacha mma open-source models.
3
Bubata na akwado
Bubata, debata, mọọbụ kesaa nsonaazụ gị. Free maka ojiji nkeonwe na nke azụmahịa.
Jiri tùlè a site na API
Megharịa ihenhọrọ a site na koodị gị. OpenAI-compatible REST endpoint, Bearer-token auth, enweghị SDK ọzọ achọrọ. Token costs match the web interface.
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: ..."}]}'Nhazigharị - FAQ
Ihe iri ise, n'ihe banyere módù ị họọrọ: (1) n'ógé - a -100 ruo +100 n'ihe nrite na n'ihe nrite; (2) 6-dimensi emó - obi ụtọ, ọ̀rà, ọ̀tụ̀tụ̀, ọ̀tụ̀tụ̀, n'ihe n'emebighị obi; (3) aspektị-báà - na-ewepụ aha ihenhọrọ mọọbụ ihenhọrọ na ihenhọrọ ọbụla n'otu n'otu; (4) n'akụkọ̀ọ̀tụ̀ / n'okpuru-okwu - na-atụgharị n'elu polarity na n'ihe a na-ahụ́ na-atọ́ ụtọ; (5) n'ihe-n'ime-okwu - táàbụ̀ okwu ọbụla n'otu n'otu. Họrọ módù nke na-ahazi ọnọdụ ịhazi gị.
Ya - a typical 200-word review runs the default Qwen 3 30B model at ~200 tokens, comfortably inside the 6,000 anonymous or 30,000 signed-up daily pool. Forensic-depth aspect analysis on longer texts costs ~600 tokens. No sign-up required to try a few.
Nwere ike ịtụle na ntụgharị zuru oke (~ 85% nkwekọrịta na ndị na-enyocha mmadụ na ntinye ederede dị mfe), n'azụ Google na nghọta nke ihe nchọgharị, n'ihu ọtụtụ na sarcasm n'ihi na anyị na-enye ụdị ihe nchọgharị na-egosipụta ihe nchọgharị. Ndị na-ere ahịa Enterprise na-akwụ ụgwọ $ 1-4 kwa 1,000 units. Advantage anyị: emotion radar + aspect-based output in one call, free, no API key to provision.
Ee - gbanwee na "Sarcasm + subtext" mode. Sistemị n'omume na-agwa módélụ̀ ka ọ gbanwee n'elu ala mgbe ọ na-ahụta ihe na-eme n'anụmanụ ("Oh great, another delay" scores negative despite positive-word presence). Nhazi na sarcasm dị n'anya bụ ~80%; n'anụmanụ na-emetụtaghị ihe bụ ihe dị njọ maka módélụ̀ ọbụla. "Mixed" verdict + low-confidence score bụ flag gị ka ịgụ ngwe ahụ onwe gị.
Instead of one verdict for the whole text, you get separate scores for each entity or feature mentioned. Example: "The hotel was beautiful but the breakfast was cold and the wifi was terrible" yields {hotel: +80, breakfast: -50, wifi: -70}. Ideal for product reviews, restaurant feedback, and NPS comments where customers mix praise and complaints in one paragraph.
99 asụsụ. The ise emotions dimensions na-abịa site Ekman basic-emotions research na-atụgharị n'ụzọ zuru ụwa ọnụ. Accuracy bụ elu na English, Spanish, French, Portuguese, German, Chinese, Japanese; ala-nri asụsụ ọrụ ma nwere ike mepụta obere ala confidence scores.
Ee - nke ahụ bụ otu n'ime ihe ndị dị elu nke na-eji ihe. Rụọ ọrụ tiketi na-abịa site na "Support ticket" mode; tiketi na-egosipụta n'okpuru -50 polarity na-arị elu-dimension gaa na nkwado gị nke elu, tiketi na-egosipụta ụzọ iji nweta ọrụ onwe gị. Ekpughe JSON site na bọtịnụ nbudata na waya na Zendesk / Freshdesk automations.
Ya - POST ka /v1/chat/ na sistem n'otu n'otu n'ime ihuakwụkwọ a na-ebubata (n'ahụ maka isi mmalite maka n'otu n'otu n'ime ihuakwụkwọ a). Na-eziga JSON nke e mepụtara. Ọ bara uru maka dashboards nke na-ewere nde ndị nke nlele n'ụbọchị. Ogo onye na-eweta, n'ụbọchị. Docs na /api/.
Confidence reflects the model certainty, not your certainty. Short inputs (<20 words), ambiguous text, and mixed-polarity content score low. A low-confidence positive is not a confident negative - it is "I do not know". Treat any score below 50 as needing human review.
UI na-agbagharị ngwe otu n'oge. Maka nnukwu, JSON+CSV nbudata na-enye gị ohere ịkpọnye API n'ime Python mọọbụ Node isiokwu nke na-ewere dataset gị. Ogo: 20,000 akara n'otu oku, oku 100/ụbọchị n'otu IP maka ndị ọrụ anonymous, 1,000/ụbọchị maka akaụntụ signed-up. Ogo ọnụọgụgụ ụlọ ọrụ dị - kpọtụrụ anyị.
MonkeyLearn bụ a na-azụta na-akwụsị. Lexalytics na Repustate ụgwọ $ 500-5,000 / mo maka dị iche iche-based + emotions nchọpụta. Anyị tool bụ free maka ọtụtụ workflows; premium models (GPT-5, Claude Sonnet) dị na-akpọ maka elu-stakes-eji ihe dị ka ego-akpọ transcripts.
A na-ejikwa ngwe n'ime-memori na GPU anyị, ọ bụghị na-echekwa na diski maka ndị ọrụ anọghị aha. Ndị ọrụ banye-n'ime na-ahụ akụkọ n'ime dashboard ha maka ụbọchị 7. Anyị anaghị akwado ngwe na ndị ọzọ ma ọ bụ jiri ya maka nkuzi. Maka nlekọta nke ọbụla-log jiri gị onwe gị API-key-gated endpoint - kpọtụrụ anyị maka SLAs nke ụlọ ọrụ.
Olee otú ị ga-esi họrọ ihenhọrọ a?