GET https://api.fastino.ai/v1/datasets
X-API-Key: YOUR_API_KEY

Optional query parameters: task_type (ner, classification, decoder), project_id.

Get a Dataset (Check It Exists)

Returns all versions of the dataset:

GET https://api.fastino.ai/v1/datasets/YOUR_DATASET_NAME
X-API-Key: YOUR_API_KEY

Returns 404 if the dataset does not exist. Use this to confirm a dataset is ready before starting training.

Dataset Row Format

Each line of your JSONL must be a single JSON object. The required columns depend on the dataset type passed as dataset_type on POST /v1/datasets/upload/url.

NER (dataset_type: "ner")

text (string) — input text
entities (list of [span, label] pairs) — each span must be a substring of text. Use [] for negative examples (rows that contain no entities).

{"text": "Ada Lovelace worked in London.", "entities": [["Ada Lovelace", "PERSON"], ["London", "LOCATION"]]}
{"text": "No tagged entities here.", "entities": []}

Classification (dataset_type: "classification")

text (string)
label (string) for single-label, OR labels (list of strings) for multi-label. Pick one and use it consistently across the entire dataset.

{"text": "Great product", "label": "positive"}
{"text": "Mixed feelings about this", "label": "neutral"}

Decoder (dataset_type: "decoder")

messages (list of {role, content} objects) — chat-style supervision. role must be one of system, user, assistant.

{"messages": [{"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "4"}]}

Fastino does not accept the GLiNER2 canonical wire shape on upload. If your data looks like {"input": ..., "output": {"entities": {"<label>": ["<span>", ...]}}} (the shape that GLiNER2 trainers consume internally), flatten it to top-level text + entities pair list before uploading. Per-label entity_descriptions are not stored on dataset rows; configure them at training-job time instead.

Upload a Dataset

Upload in two steps: get a presigned URL, then process the uploaded file.

Step 1 — get presigned upload URL:

POST https://api.fastino.ai/v1/datasets/upload/url
X-API-Key: YOUR_API_KEY
Content-Type: application/json

{
  "dataset_name": "my-dataset",
  "file_name": "data.jsonl"
}

Returns upload_url (PUT to this with the file body) and dataset_id.

Step 2 — process after upload:

POST https://api.fastino.ai/v1/datasets/upload/process
X-API-Key: YOUR_API_KEY
Content-Type: application/json

{
  "dataset_id": "DATASET_ID_FROM_STEP_1"
}

Returns HTTP 202 immediately. Poll GET /v1/datasets/{name} until the version appears.

Delete an Entire Dataset (All Versions)

DELETE https://api.fastino.ai/v1/datasets/YOUR_DATASET_NAME
X-API-Key: YOUR_API_KEY

Use a Dataset for Training

Pass the dataset name directly to POST /v1/training-jobs:

{
  "datasets": [{"name": "YOUR_DATASET_NAME"}]
}

See the fastino-fine-tune skill for the full training workflow.

Error Codes

Code	Meaning
401	Invalid or missing API key
402	Insufficient credits
404	Dataset not found
422	Validation error — check request body fields
500	Server error — safe to retry

fastino-datasets

Fastino Datasets

Authentication

List Datasets