Prepare and upload training datasets

Training jobs reference ready datasets by name. Prepare a JSONL file, upload it through the two-step dataset API, and confirm the processed version exists before calling POST /v1/training/jobs.

Choose a dataset format

Each line in the file must be one JSON object. Use one consistent shape throughout a dataset.

Named entity recognition

Use text plus an entities list of [span, label] pairs. Every span must appear in the text, but matching is case-insensitive: "ada lovelace" is valid for "Ada Lovelace". Include negative examples with an empty list.

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

Classification

Use label for single-label data or labels for multi-label data. Use the same field in every row.

{"text":"The setup was straightforward.","label":"positive"}
{"text":"The request needs technical help.","label":"technical"}

GLiNER2 library format

NER and classification uploads also accept GLiNER2’s canonical input and output shape. For NER, map each entity label to its matching spans:

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

For classification, classifications can contain one or several tasks in each row:

{"input":"jump now","output":{"classifications":[{"task":"action","labels":["jump","wait"],"true_label":["jump"]},{"task":"urgency","labels":["now","later"],"true_label":["now"]}]}}

Use one shape per row. Do not mix text, label, labels, or entities with input and output fields in the same row.

Joint NER and classification

A row can contain both entities and label to train both heads. Keep that same joint shape in every row, and do not mix single-label label with multi-label labels.

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

Decoder

Set dataset_type to decoder and give every row a messages array with chat roles and content.

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

The dataset service accepts decoder datasets for fine-tune-agent and storage workflows. Config jobs reject decoder base models, so don’t treat dataset_type: decoder as a direct config-job decoder-training path. Interactive jobs don’t accept a dataset name; read and tokenize the examples in your application, then send tokenized datums as shown in the Decoder Quickstart.

Upload a dataset

1. Reserve an upload URL

curl --fail-with-body -sS https://api.fastino.ai/v1/datasets/upload/url \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "dataset_name": "docs-example-ner",
    "filename": "training.jsonl",
    "dataset_type": "ner"
  }' | tee dataset-upload.json

export PRESIGNED_URL="$(jq -r '.presigned_url' dataset-upload.json)"
export DATASET_ID="$(jq -r '.dataset_id' dataset-upload.json)"
export DATASET_VERSION="$(jq -r '.version_number' dataset-upload.json)"

The response contains a short-lived presigned_url, a dataset_id, and a version_number. The commands above retain all three values. Upload the exact file bytes to the presigned URL:

curl --fail-with-body -X PUT \
  -H "Content-Type: application/octet-stream" \
  --upload-file training.jsonl "$PRESIGNED_URL"

2. Process the uploaded file

curl --fail-with-body -sS https://api.fastino.ai/v1/datasets/upload/process \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d "$(jq -n --arg dataset_id "$DATASET_ID" '{dataset_id: $dataset_id}')"

Processing returns 202. Poll the dataset by name and select the entry whose version_number exactly matches the upload response. Continue until that version’s status is ready or failed. If it is failed, stop and surface its processing_error; do not submit that version to training.

curl --fail-with-body -sS https://api.fastino.ai/v1/datasets/docs-example-ner \
  -H "X-API-Key: $FASTINO_API_KEY"

Start training

Pass the exact dataset name to the training-job create endpoint:

{
  "model_name": "my-ner-model",
  "base_model": "fastino/gliner2-base-v1",
  "datasets": [
    {"name": "docs-example-ner", "version": "VERSION_NUMBER_FROM_UPLOAD"}
  ],
  "training_type": "lora"
}

Use GET /v1/base-models?supports_training=true immediately before submission to select a currently trainable base model.

Common blockers

Status	Cause	Action
`401`	Missing or invalid API key	Send `X-API-Key: $FASTINO_API_KEY`
`404`	Dataset name does not exist	Confirm the exact name and wait for processing
`failed`	Asynchronous content validation failed	Read `processing_error`, correct the JSONL, and upload a new version
`422`	The request shape was rejected before processing	Correct the request fields before retrying

Get Started

Decision Models

Training API

GLiNER Models

Inference API

Troubleshooting

Models

Account

Prepare and upload training datasets

Choose a dataset format

Named entity recognition

Classification

GLiNER2 library format

Joint NER and classification

Decoder

Upload a dataset

1. Reserve an upload URL

2. Process the uploaded file

Start training

Common blockers

Dataset API contracts

Upload URL

Process upload

List datasets

Dataset versions

Delete dataset

​Choose a dataset format

​Named entity recognition

​Classification

​GLiNER2 library format

​Joint NER and classification

​Decoder

​Upload a dataset

​1. Reserve an upload URL

​2. Process the uploaded file

​Start training

​Common blockers

​Dataset API contracts

Upload URL

Process upload

List datasets

Dataset versions

Delete dataset

Choose a dataset format

Named entity recognition

Classification

GLiNER2 library format

Joint NER and classification

Decoder

Upload a dataset

1. Reserve an upload URL

2. Process the uploaded file

Start training

Common blockers

Dataset API contracts