> ## Documentation Index > Fetch the complete documentation index at: https://docs.zerogpu.ai/llms.txt > Use this file to discover all available pages before exploring further. # gliner2.5-base-v1 > Model details for gliner2.5-base-v1. English schema-based information extraction built on DeBERTa-v3-base. > Fastino's GLiNER2.5 Base is an English schema-based information extraction model built on DeBERTa-v3-base. A single model covers entity extraction, text classification, structured record extraction, relation extraction, and span-level attributes — you supply a label set or a schema at inference time instead of fine-tuning one model per task. It fits GTM workflows such as parsing inbound leads, extracting company and contact details, classifying sales conversations, identifying relationships between people and companies, structuring CRM notes, and enriching customer or account data. **References:** [Model docs](https://huggingface.co/fastino/gliner2.5-base-v1) • [Terms](https://github.com/fastino-ai/GLiNER2/blob/main/LICENSE) • [Privacy](https://github.com/fastino-ai/GLiNER2/blob/main/LICENSE) ## OpenAPI ````yaml api-reference/openapi/playgrounds/gliner2_5-base-v1.openapi.json POST /responses openapi: 3.1.0 info: title: gliner2.5-base-v1 playground version: '1.0' description: >- Interactive playground for **gliner2.5-base-v1**. Model is always `gliner2.5-base-v1` on this page (shown in the form, not editable). Use the **request example** selector to switch use cases (NER, JSON, classification). Authentication: `x-api-key` (required) and `x-project-id` (optional). servers: - url: https://api.zerogpu.ai/v1 description: Production security: - ApiKey: [] paths: /responses: post: tags: - gliner2.5-base-v1 summary: 'gliner2.5-base-v1: Responses' operationId: createResponse_gliner2_5-base-v1 parameters: - name: x-project-id in: header required: false schema: type: string description: >- Optional project identifier. Scopes the request to a specific project when provided. requestBody: required: true content: application/json: schema: allOf: - $ref: '#/components/schemas/CreateResponseRequest' - type: object properties: model: type: string const: gliner2.5-base-v1 default: gliner2.5-base-v1 example: gliner2.5-base-v1 description: >- Model identifier (fixed for this playground). Use request examples to change use cases. examples: ner: summary: Entity Extraction value: input: >- The application is built with Python 3.11 and uses PostgreSQL 15 for storage. It runs on Kubernetes with Docker containers and communicates via gRPC. model: gliner2.5-base-v1 metadata: labels: - programming language - database - technology - protocol usecase: ner threshold: 0.3 json: summary: Structured Data Extraction value: input: >- Best regards, John Smith, Senior Software Engineer at Acme Corp. Phone: (555) 123-4567, Email: john.smith@acme.com, Office: 123 Main Street, Suite 400, San Francisco, CA 94105 model: gliner2.5-base-v1 metadata: schema: contact: - name::str::Full name - title::str::Job title - company::str::Company name - phone::str::Phone number - email::str::Email address - address::str::Office address usecase: json classification: summary: Text Classification value: input: >- I absolutely love this product! The quality is outstanding and the customer service was incredibly helpful. model: gliner2.5-base-v1 metadata: schema: sentiment: - positive - negative - neutral usecase: classification responses: '200': description: Success content: application/json: schema: $ref: '#/components/schemas/Response' '400': description: Bad request '401': description: Unauthorized '403': description: Forbidden '420': description: Insufficient quota content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '500': description: Internal server error components: schemas: CreateResponseRequest: type: object required: - model - input properties: model: type: string const: gliner2.5-base-v1 default: gliner2.5-base-v1 example: gliner2.5-base-v1 description: >- Model identifier (fixed for this playground). Use request examples to change use cases. input: type: string minLength: 1 format: textarea maxLength: 131072 description: Multi-line text or document content to send to the model. metadata: type: object description: >- Use-case options for the model. Three use cases are supported: - `ner` — extract entities for the given `labels`. - `json` — extract structured fields defined by a `schema`. - `classification` — assign labels from candidate sets defined by a `schema`. additionalProperties: true properties: usecase: type: string enum: - ner - json - classification default: ner description: >- Which operation to run and which other fields apply: `ner` uses `labels`; `json` and `classification` use `schema`. labels: type: array items: type: string example: - programming language - database - technology - protocol description: >- Entity types to extract, used when `usecase` is `ner`. Each label becomes a key in the returned `entities` object. threshold: type: number minimum: 0 maximum: 1 default: 0.5 description: >- Minimum confidence score (0–1) an entity must reach to be returned (used with `ner`). Lower values surface more, lower-confidence matches. schema: type: object additionalProperties: type: array items: type: string description: >- Extraction/classification definition, used when `usecase` is `json` or `classification`. For `json`, each group maps to field definitions in `name::type::description` form (e.g. `"email::str::Email address"`). For `classification`, each group maps to a list of candidate labels (e.g. `["positive", "negative", "neutral"]`). example: contact: - name::str::Full name - email::str::Email address - phone::str::Phone number Response: type: object additionalProperties: true ErrorResponse: type: object additionalProperties: true securitySchemes: ApiKey: type: apiKey in: header name: x-api-key ````