all-MiniLM-L12-v2 is a Sentence Transformers encoder for sentences and short paragraphs. It produces one 384-dimensional dense vector per text for semantic search, information retrieval, clustering and sentence similarity. The model uses the MiniLM-L12-H384-uncased backbone and was fine-tuned with contrastive learning on a billion-pair sentence collection.
Use a sentence or a short paragraph that expresses one useful idea. For a collection of product descriptions, keep the feature and the condition that distinguishes the item: “The adapter connects a wired headset to a USB-C port” is more informative than “Audio accessory.” Store the record identifier and original description beside its vector.
A search query can then be compared with the encoded descriptions. Inspect the returned candidates for exact requirements such as connector type or device compatibility before presenting a final choice. The vector represents the text’s meaning; the surrounding search application retrieves the corresponding records.
The published encoding example averages token representations with the attention mask, so padding positions do not contribute to the mean. It then applies L2 normalization. These steps are part of the reference recipe for obtaining the sentence vector, rather than an instruction to take an arbitrary token from the underlying transformer.
Keep the model, pooling and normalization configuration consistent between indexed texts and new queries. The resulting 384-component representations can be compared using cosine similarity. Keep a record of that configuration with the collection; a different 384-dimensional encoder does not become compatible simply because its vectors have the same length.
The maker specifies default truncation beyond 256 word pieces. This is a tokenizer limit, not a count of ordinary words or characters. For a longer record, choose coherent short sections and preserve the heading or identifying detail needed to interpret each one.
The model’s training procedure used sequences of up to 128 tokens. That training setting is separate from the published 256-word-piece default at encoding time. A passage that fits the default can still differ substantially from the short sentence material used during training, so assess long or specialised descriptions with representative examples.
For a set of support messages, embeddings can supply numerical inputs to a grouping stage. Review several messages from each proposed group and those near its boundary before assigning a human-readable label. For duplicate-content review, retrieve similar records and compare their actual wording; two passages may discuss the same subject while giving different instructions.
This exact L12-v2 edition is an English sentence-embedding profile. It is distinct from all-MiniLM-L6-v2 and the paraphrase-MiniLM checkpoints. Shared family names do not transfer their sequence configuration or pooling recipe. Use its compact dense vectors together with source records and task-specific evaluation, rather than treating a similarity value as a verified answer or a final editorial decision.
", "aboutText": "all-MiniLM-L12-v2 is a Sentence Transformers encoder for sentences and short paragraphs. It produces one 384-dimensional dense vector per text for semantic search, information retrieval, clustering and sentence similarity. The model uses the MiniLM-L12-H384-uncased backbone and was fine-tuned with contrastive learning on a billion-pair sentence collection.\n\n### Encode the unit you want to retrieve\n\nUse a sentence or a short paragraph that expresses one useful idea. For a collection of product descriptions, keep the feature and the condition that distinguishes the item: “The adapter connects a wired headset to a USB-C port” is more informative than “Audio accessory.” Store the record identifier and original description beside its vector.\n\nA search query can then be compared with the encoded descriptions. Inspect the returned candidates for exact requirements such as connector type or device compatibility before presenting a final choice. The vector represents the text’s meaning; the surrounding search application retrieves the corresponding records.\n\n### Mean pooling and normalized vectors\n\nThe published encoding example averages token representations with the attention mask, so padding positions do not contribute to the mean. It then applies L2 normalization. These steps are part of the reference recipe for obtaining the sentence vector, rather than an instruction to take an arbitrary token from the underlying transformer.\n\nKeep the model, pooling and normalization configuration consistent between indexed texts and new queries. The resulting 384-component representations can be compared using cosine similarity. Keep a record of that configuration with the collection; a different 384-dimensional encoder does not become compatible simply because its vectors have the same length.\n\n### Default input length and training scope\n\nThe maker specifies default truncation beyond 256 word pieces. This is a tokenizer limit, not a count of ordinary words or characters. For a longer record, choose coherent short sections and preserve the heading or identifying detail needed to interpret each one.\n\nThe model’s training procedure used sequences of up to 128 tokens. That training setting is separate from the published 256-word-piece default at encoding time. A passage that fits the default can still differ substantially from the short sentence material used during training, so assess long or specialised descriptions with representative examples.\n\n### Similarity and clustering workflows\n\nFor a set of support messages, embeddings can supply numerical inputs to a grouping stage. Review several messages from each proposed group and those near its boundary before assigning a human-readable label. For duplicate-content review, retrieve similar records and compare their actual wording; two passages may discuss the same subject while giving different instructions.\n\nThis exact L12-v2 edition is an English sentence-embedding profile. It is distinct from all-MiniLM-L6-v2 and the paraphrase-MiniLM checkpoints. Shared family names do not transfer their sequence configuration or pooling recipe. Use its compact dense vectors together with source records and task-specific evaluation, rather than treating a similarity value as a verified answer or a final editorial decision.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "eeee7bb3fb95a93aae0f191e074dc1fce7fa8626f30cbbdc57e5eac6263b52f1", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "sentence-transformers/all-minilm-l12-v2Sentence Transformers all-MiniLM-L12-v2 encodes sentences and short paragraphs into 384-dimensional vectors for search, similarity and clustering. Its reference recipe uses mean pooling and normalization, with default truncation at 256 word pieces.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
all-MiniLM-L12-v2 is a Sentence Transformers encoder for sentences and short paragraphs. It produces one 384-dimensional dense vector per text for semantic search, information retrieval, clustering and sentence similarity. The model uses the MiniLM-L12-H384-uncased backbone and was fine-tuned with contrastive learning on a billion-pair sentence collection.
Use a sentence or a short paragraph that expresses one useful idea. For a collection of product descriptions, keep the feature and the condition that distinguishes the item: “The adapter connects a wired headset to a USB-C port” is more informative than “Audio accessory.” Store the record identifier and original description beside its vector.
A search query can then be compared with the encoded descriptions. Inspect the returned candidates for exact requirements such as connector type or device compatibility before presenting a final choice. The vector represents the text’s meaning; the surrounding search application retrieves the corresponding records.
The published encoding example averages token representations with the attention mask, so padding positions do not contribute to the mean. It then applies L2 normalization. These steps are part of the reference recipe for obtaining the sentence vector, rather than an instruction to take an arbitrary token from the underlying transformer.
Keep the model, pooling and normalization configuration consistent between indexed texts and new queries. The resulting 384-component representations can be compared using cosine similarity. Keep a record of that configuration with the collection; a different 384-dimensional encoder does not become compatible simply because its vectors have the same length.
The maker specifies default truncation beyond 256 word pieces. This is a tokenizer limit, not a count of ordinary words or characters. For a longer record, choose coherent short sections and preserve the heading or identifying detail needed to interpret each one.
The model’s training procedure used sequences of up to 128 tokens. That training setting is separate from the published 256-word-piece default at encoding time. A passage that fits the default can still differ substantially from the short sentence material used during training, so assess long or specialised descriptions with representative examples.
For a set of support messages, embeddings can supply numerical inputs to a grouping stage. Review several messages from each proposed group and those near its boundary before assigning a human-readable label. For duplicate-content review, retrieve similar records and compare their actual wording; two passages may discuss the same subject while giving different instructions.
This exact L12-v2 edition is an English sentence-embedding profile. It is distinct from all-MiniLM-L6-v2 and the paraphrase-MiniLM checkpoints. Shared family names do not transfer their sequence configuration or pooling recipe. Use its compact dense vectors together with source records and task-specific evaluation, rather than treating a similarity value as a verified answer or a final editorial decision.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits sentence-transformers/all-minilm-l12-v2 | Default— |
Fieldinput | Typestring|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— |
Fieldencoding_format | Typestring | RequirementOptional | Values & limits float | Defaultfloat |
Fielddimensions | Typeinteger | RequirementNot supported | Values & limits Minimum: 1 | Default— |
Fielduser | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldsession_id | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldtrace | Typejson_value | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— |
text_to_embedding
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input |
Parameterinput_shapes | Contractstring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[] |
Parameterencoding_format | Contractfloat |
Parameterdimensions_override | Contractfalse |
Parametermax_input_budget_units | Contract512 |
Parametermultimodal_objects | Contractfalse |
Parametermaterial_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.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.00001 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "sentence-transformers/all-minilm-l12-v2", "input": ["First document", "Second document"]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.embeddings.create(model='sentence-transformers/all-minilm-l12-v2',input=["First document","Second document"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\njson
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.
No embedding job endpoint is introduced.
| Field | Type |
|---|---|
Fieldobject | Typestring |
Fieldmodel | Typestring |
Fielddata[].index | Typeinteger |
Fielddata[].embedding | Typefinite float[] |
Fieldusage | Typeobject |
| 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. |
Ailoot Ultra sentence-transformers/all-minilm-l12-v2
Use the method paths shown above.