Source: https://ailoot.ai/models/en/sentence-transformers-all-minilm-l6-v2/
Canonical: https://ailoot.ai/models/en/sentence-transformers-all-minilm-l6-v2/
Language: en

# Sentence Transformers: all-MiniLM-L6-v2

[Markdown](https://ailoot.ai/models/en/sentence-transformers-all-minilm-l6-v2/index.md) · [JSON](https://ailoot.ai/models/en/sentence-transformers-all-minilm-l6-v2/facts.json)

## [Sentence Transformers: all-MiniLM-L6-v2](https://ailoot.ai/models/en/sentence-transformers-all-minilm-l6-v2/)

Sentence Transformers all-MiniLM-L6-v2 represents sentences and short paragraphs as 384-dimensional vectors for semantic search, clustering and similarity. Its published recipe combines mean pooling with L2 normalization and a 256-word-piece default.

- [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `sentence-transformers/all-minilm-l6-v2`](https://ailoot.ai/models/en/sentence-transformers-all-minilm-l6-v2/#method-0)

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

all-MiniLM-L6-v2 is a Sentence Transformers sentence and short-paragraph encoder. It maps each text to a 384-dimensional dense vector for retrieval, clustering and sentence similarity. The model was fine-tuned from the MiniLM-L6-H384-uncased backbone using contrastive learning on a billion-pair sentence collection.

#### Prepare concise, meaningful records

Choose the sentence or paragraph that should appear in a search result. For an FAQ collection, retain the question and the short explanation that distinguishes its topic. A passage about resetting a forgotten password should keep the reset procedure, rather than being reduced to an isolated word such as “Account.”

For a long article, index coherent short sections and keep their source locations. Retrieve candidate sections by comparing the query’s vector with the stored representations, then show the original passages so the reader can inspect the actual instructions.

#### Obtain the sentence representation

The maker’s direct encoding recipe averages token representations with the attention mask, excluding padding positions from the mean. It then applies L2 normalization. Those steps produce the sentence embedding used for comparison; individual token representations from the underlying transformer serve a different role.

Each output has 384 components. Use the same checkpoint, pooling and normalization when building the collection and encoding later queries. Cosine similarity can compare the resulting vectors. A similarity value is useful for ranking candidates under that setup, while the original text determines whether a candidate answers the question.

#### Length and edition boundaries

The published default truncates inputs longer than 256 word pieces. This tokenizer unit does not correspond directly to a normal word or character. Keep important conditions inside the indexed unit, and split a lengthy procedure at meaningful boundaries before encoding it.

Training sequences were limited to 128 tokens. That training setting is separate from the 256-word-piece default used for encoding. For unusually long or specialised passages, use representative query-and-passage examples to assess the retrieval behavior rather than assuming that fitting the input boundary guarantees equal treatment of every detail.

The exact L6-v2 edition is an English embedding profile. The L12-v2 and paraphrase-MiniLM editions have separate model identities and configurations. Their common MiniLM name or 384-dimensional output does not establish interchangeable vectors.

#### Search, similarity and grouping

For duplicate-question review, retrieve similar questions and compare the actual wording before merging records. “Can I reset my password?” and “Can an administrator reset another user’s password?” concern related subjects but different permissions. Preserve that distinction in the encoded text and in the review stage.

For clustering, use the embeddings as numerical inputs to a grouping process, then inspect representative records before naming a group. This model supplies vectors rather than written cluster labels or a generated FAQ answer. Its useful result is a compact representation of a short source passage that remains linked to the original record.

**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `sentence-transformers/all-minilm-l6-v2`

[`sentence-transformers/all-minilm-l6-v2`](https://ailoot.ai/models/en/#m=sentence-transformers%2Fall-minilm-l6-v2&route=Ultra&caller=sentence-transformers%2Fall-minilm-l6-v2&fmt=embeddings)

Sentence Transformers /`sentence-transformers/all-minilm-l6-v2`

### Overview

Sentence Transformers all-MiniLM-L6-v2 represents sentences and short paragraphs as 384-dimensional vectors for semantic search, clustering and similarity. Its published recipe combines mean pooling with L2 normalization and a 256-word-piece default.

Accepted inputs

Text · token\_ids

Results

embedding

### Available in Ailoot · inputs & methods

**Ailoot Ultra · Embeddings** `POST /v1/embeddings`

Input types

`text`, `token_ids`

Output types

`embedding`

Output format

json

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits sentence-transformers/all-minilm-l6-v2 | Default— |
| Field`input` | Type`string\|string[]\|integer[]\|integer[][]` | RequirementRequired | Values & limits Minimum items: 1 Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers. Input budget: Maximum: 512 · Text unit: UTF8 bytes · Token unit: token-array elements | Default— |
| Field`encoding_format` | Type`string` | RequirementOptional | Values & limits float | Defaultfloat |
| Field`dimensions` | Type`integer` | RequirementNot supported | Values & limits Minimum: 1 | Default— |
| Field`user` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`session_id` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`trace` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
| Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |

Input material combinations

**text\_to\_embedding**

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractmodel, input |
| Parameter`input_shapes` | Contractstring, string\[\], nonnegative\_integer\_token\_array, nonnegative\_integer\_token\_array\[\] |
| Parameter`encoding_format` | Contractfloat |
| Parameter`dimensions_override` | Contractfalse |
| Parameter`max_input_budget_units` | Contract512 |
| Parameter`multimodal_objects` | Contractfalse |
| Parameter`material_types` | Contracttext, token\_ids |

Lifecycle: `synchronous_json`

Keep every vector index and dimension.

Multimodal model name does not admit image objects on this exact path.

### Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode

Not specified When fast mode is selected

Flex mode

Not specified When flex mode is selected

Maximum reasoning effort

Not specified When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

| Configuration | Rate schedule | Charge |
| --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.00001** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · Embeddings

curlPython SDK

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "sentence-transformers/all-minilm-l6-v2", "input": ["First document", "Second document"]}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))
PY_RESULT
```

```python
from openai import OpenAI
import os

client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.embeddings.create(model='sentence-transformers/all-minilm-l6-v2',input=["First document","Second document"])
print(result)
for item in result.data:print(item.index,item.embedding)
```

### Receive and save results

#### Ailoot Ultra · Embeddings

json

##### Keep all completed results

Keep every complete returned response, file and content part.

Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.

##### If the original response needs checking

No embedding job endpoint is introduced.

Response fields

| Field | Type |
| --- | --- |
| Field`object` | Typestring |
| Field`model` | Typestring |
| Field`data[].index` | Typeinteger |
| Field`data[].embedding` | Typefinite float\[\] |
| Field`usage` | Typeobject |

Errors and next steps

| Response | Condition | Next step |
| --- | --- | --- |
| Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |

### Aliases

#### Model aliases

**Ailoot Ultra** [`sentence-transformers/all-minilm-l6-v2`](https://ailoot.ai/models/en/#m=sentence-transformers%2Fall-minilm-l6-v2&route=Ultra&caller=sentence-transformers%2Fall-minilm-l6-v2&fmt=embeddings)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`sentence-transformers/all-minilm-l6-v2`](https://ailoot.ai/models/en/#m=sentence-transformers%2Fall-minilm-l6-v2&route=Ultra&caller=sentence-transformers%2Fall-minilm-l6-v2&fmt=embeddings)

FAQ · Sentence Transformers: all-MiniLM-L6-v2

What inputs and results are available?

`POST /v1/embeddings` · `sentence-transformers/all-minilm-l6-v2`: `text`, `token_ids` → `embedding`

Which fields does the request use?

`POST /v1/embeddings` · `sentence-transformers/all-minilm-l6-v2`: `model`, `input`, `encoding_format`, `provider`

What limits apply?

`POST /v1/embeddings` · `sentence-transformers/all-minilm-l6-v2`: `input.budget.maximum`: `512` (UTF8 bytes); `input.min_items`: `1`

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