Finalize labeling job
Turn the rows you accepted into a dataset.
curl --request POST \
--url https://api.sqwish.ai/v1/labeling/jobs/{job_id}/finalize \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"row_indices": [
123
],
"evaluation_fraction": 0.55,
"training_indices": [
123
],
"evaluation_indices": [
123
],
"max_price_usd": "<string>"
}
'import requests
url = "https://api.sqwish.ai/v1/labeling/jobs/{job_id}/finalize"
payload = {
"row_indices": [123],
"evaluation_fraction": 0.55,
"training_indices": [123],
"evaluation_indices": [123],
"max_price_usd": "<string>"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
row_indices: [123],
evaluation_fraction: 0.55,
training_indices: [123],
evaluation_indices: [123],
max_price_usd: '<string>'
})
};
fetch('https://api.sqwish.ai/v1/labeling/jobs/{job_id}/finalize', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"id": "<string>",
"name": "<string>",
"created_at": 123,
"sha256": "<string>",
"version": 123,
"rows": 123,
"contexts": 123,
"duplicates_removed": 123,
"method": "sft",
"archived_at": 123,
"used_at": 123,
"evaluation_only": true,
"evaluation_fraction": 123,
"split": {},
"prompt_tuning": {
"decisions": [
{
"decision": {
"id": "<string>",
"question": "<string>",
"kind": "single",
"outcomes": "<unknown>",
"rubric": [
"<unknown>"
],
"policy": {
"action_costs": {},
"abstain_below": 0.5
}
},
"outcomes": [
"<string>"
],
"rows": 123,
"labels": {},
"reason": "conflicting_decision"
}
],
"unlisted": 123
},
"input": {
"format": "json",
"sha256": "<string>",
"bytes": 123
},
"labeling": {
"job_id": "<string>",
"source_id": "<string>",
"source_sha256": "<string>",
"source_input_sha256": "<string>",
"spec_sha256": "<string>",
"prompt_version": "<string>",
"teacher_models": [
"<string>"
],
"row_indices": [
123
],
"selection_sha256": "<string>",
"target_method": "mean_one_hot_teacher_votes"
},
"storage_window": {
"started_at": 123,
"ends_at": 123,
"reserved_usd": "<string>"
},
"labels_to_check": 123
}{
"error": {
"code": "<string>",
"message": "<string>",
"retryable": true,
"request_id": "<string>",
"details": [
{
"path": [
"<string>"
],
"message": "<string>",
"type": "<string>"
}
]
}
}{
"error": {
"code": "<string>",
"message": "<string>",
"retryable": true,
"request_id": "<string>",
"details": [
{
"path": [
"<string>"
],
"message": "<string>",
"type": "<string>"
}
]
}
}Authorizations
Create an API key in the console.
Headers
Path Parameters
Body
The rows a job's dataset takes, in one of two modes, as a dataset upload: row_indices,
whose sides the split picks at evaluation_fraction, or training_indices and
evaluation_indices, whose rows take the side of their list.
20000.1 <= x <= 120002000The most you agree to pay for its first day of storage, which a dataset above the free size holds, in USD, such as the price you were shown. It is a decimal string, such as "2.500000000", never a JSON number, so no precision is lost: a number is refused with invalid_request, naming this field. If it costs more when it is admitted, it is refused with price_changed and nothing is held.
^\d{1,9}(\.\d{1,9})?$Response
Successful Response
Names every row of the latest version: its first version's file digest, each later one's chained on.
The latest version. Counts and the split are this version's.
sft, reward When we archived it: when credit couldn't cover its storage, or on a suspension. Restore it to start new work. Null in the library.
When work last started on it: a fine-tune, an evaluation or prompt tuning, or the labelling job that made it. Adding rows doesn't count. Null until then.
Its first version's evaluation share: as asked, or for a split of your own its evaluation contexts' share, which may be below the 0.1 a request takes.
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Show child attributes
What prompt tuning can tune in the training partition, counted when the dataset was made or grew, with the reasons the counts give worked out under the limits in force when it is read. Datasets of outcome scores have none.
Show child attributes
Show child attributes
Show child attributes
Show child attributes
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Show child attributes
The day of storage held now, at the price it was accepted at, or null. Archiving or deleting it charges the minutes used of it; later days are priced as they start.
Show child attributes
Show child attributes
How many labels prompt tuning doubted in this dataset that nobody has dismissed, each row and decision once, as its labels-to-check route lists them.
curl --request POST \
--url https://api.sqwish.ai/v1/labeling/jobs/{job_id}/finalize \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"row_indices": [
123
],
"evaluation_fraction": 0.55,
"training_indices": [
123
],
"evaluation_indices": [
123
],
"max_price_usd": "<string>"
}
'import requests
url = "https://api.sqwish.ai/v1/labeling/jobs/{job_id}/finalize"
payload = {
"row_indices": [123],
"evaluation_fraction": 0.55,
"training_indices": [123],
"evaluation_indices": [123],
"max_price_usd": "<string>"
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
row_indices: [123],
evaluation_fraction: 0.55,
training_indices: [123],
evaluation_indices: [123],
max_price_usd: '<string>'
})
};
fetch('https://api.sqwish.ai/v1/labeling/jobs/{job_id}/finalize', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"id": "<string>",
"name": "<string>",
"created_at": 123,
"sha256": "<string>",
"version": 123,
"rows": 123,
"contexts": 123,
"duplicates_removed": 123,
"method": "sft",
"archived_at": 123,
"used_at": 123,
"evaluation_only": true,
"evaluation_fraction": 123,
"split": {},
"prompt_tuning": {
"decisions": [
{
"decision": {
"id": "<string>",
"question": "<string>",
"kind": "single",
"outcomes": "<unknown>",
"rubric": [
"<unknown>"
],
"policy": {
"action_costs": {},
"abstain_below": 0.5
}
},
"outcomes": [
"<string>"
],
"rows": 123,
"labels": {},
"reason": "conflicting_decision"
}
],
"unlisted": 123
},
"input": {
"format": "json",
"sha256": "<string>",
"bytes": 123
},
"labeling": {
"job_id": "<string>",
"source_id": "<string>",
"source_sha256": "<string>",
"source_input_sha256": "<string>",
"spec_sha256": "<string>",
"prompt_version": "<string>",
"teacher_models": [
"<string>"
],
"row_indices": [
123
],
"selection_sha256": "<string>",
"target_method": "mean_one_hot_teacher_votes"
},
"storage_window": {
"started_at": 123,
"ends_at": 123,
"reserved_usd": "<string>"
},
"labels_to_check": 123
}{
"error": {
"code": "<string>",
"message": "<string>",
"retryable": true,
"request_id": "<string>",
"details": [
{
"path": [
"<string>"
],
"message": "<string>",
"type": "<string>"
}
]
}
}{
"error": {
"code": "<string>",
"message": "<string>",
"retryable": true,
"request_id": "<string>",
"details": [
{
"path": [
"<string>"
],
"message": "<string>",
"type": "<string>"
}
]
}
}