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Thenlper: GTE-Base

thenlper/gte-base

1KContext Window

Supported Protocols:max_tokenstemperaturetop_pstopfrequency_penaltypresence_penaltyrepetition_penaltytop_kseedmin_presponse_format

Online

The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.

Capabilities

Text GenerationCode GenerationAnalysis & Reasoningmodels.reasoning

Technical Specs

Input Modality

Text

Output Modality

Text

Arch

—

Pricing

Pay per use, no monthly fees

Input Token< ¥0.001/1K Token

Output Token< ¥0.001/1K Token

Quick Start

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uniontoken.ai/v1",
    api_key="YOUR_UNIONTOKEN_API_KEY",
)

response = client.chat.completions.create(
    model="thenlper/gte-base",
    messages=[
        {"role": "user", "content": "Hello!"}
    ],
)

print(response.choices[0].message.content)

FAQ

Thenlper: GTE-Base

thenlper/gte-base

In< ¥0.001/1K

Out< ¥0.001/1K

Context Window1K

Start Using →View Integration Docs

Related Models

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