Multilingual-E5-Large is intfloat’s multilingual dense text embedding model, with twenty-four layers and a 1024-dimensional output. It is initialised from XLM-RoBERTa Large and trained further on multilingual data. The model card describes support for the hundred-language XLM-R scope while noting weaker performance for low-resource languages. Its vectors represent text for retrieval, semantic comparison and numerical-feature tasks.
The trained prefixes are “query:” and “passage:”, including for non-English text. A retrieval query and its corpus passages use their corresponding roles. The prefixes remain those English strings while the source wording retains its own language. For symmetric semantic similarity, paraphrase retrieval, bitext mining or feature use, the maker recommends “query:”.
A practical collection can hold documentation in several supported languages. Retain each passage’s language, original text and source location with its vector. Prepare real search questions from the audience rather than assuming that one English test represents the whole collection. The output should return the actual passage and its context so the reader can assess what it says.
For a multilingual matching task, keep the purpose of the comparison clear. A candidate pair of translations and a query looking for a supporting passage are different preparations. The model’s role-prefix guidance supplies a concrete convention for those tasks; omitting the prefix is not the documented equivalent because it changes the input pattern used in training.
The maker’s example averages token representations using an attention mask to exclude padding and then normalises the vector. Normalised dot products give cosine similarity. The 1024-dimensional array represents one prepared text. Keep the exact model and preparation associated with the stored vectors so later queries use the matching representation.
These vectors can supply candidate rankings or features for a later clustering or classification stage. They do not translate the source or compose an answer. Return the underlying passage text when a retrieval result is meant to support a reader or a later RAG generation stage.
The model card documents weakly supervised contrastive pretraining followed by supervised fine-tuning. The multilingual preparation includes material such as title–passage pairs, conversational pairs and translation pairs, while labelled retrieval and language-understanding data provide the later stage. This explains the intended text-representation tasks without establishing equal performance in every language.
The maker explicitly identifies low-resource-language degradation as a limitation. Check the actual languages and wording in the collection, including abbreviations, names and specialised vocabulary. A multilingual label is a useful scope description; the relevance of the returned text still needs to be examined for the audience and task.
Long input is truncated to at most 512 tokens. Prepare a lengthy document as coherent passages with the role prefix and relevant headings included. Preserve definitions and qualifications beside the statement they affect, and retain a parent-document identifier and source version outside the vector.
The manufacturer recommends focusing on relative cosine-score order rather than treating an absolute value as a universal confidence measure. A useful result is a candidate passage set, comparable text representations or features linked to their original records. This exact model remains separate from the English-only E5 v2 checkpoints and from instruction-tuned E5 variants, even when another model also returns 1024 values.
", "aboutText": "Multilingual-E5-Large is intfloat’s multilingual dense text embedding model, with twenty-four layers and a 1024-dimensional output. It is initialised from XLM-RoBERTa Large and trained further on multilingual data. The model card describes support for the hundred-language XLM-R scope while noting weaker performance for low-resource languages. Its vectors represent text for retrieval, semantic comparison and numerical-feature tasks.\n\n### Preserve the input role in every language\n\nThe trained prefixes are “query:” and “passage:”, including for non-English text. A retrieval query and its corpus passages use their corresponding roles. The prefixes remain those English strings while the source wording retains its own language. For symmetric semantic similarity, paraphrase retrieval, bitext mining or feature use, the maker recommends “query:”.\n\nA practical collection can hold documentation in several supported languages. Retain each passage’s language, original text and source location with its vector. Prepare real search questions from the audience rather than assuming that one English test represents the whole collection. The output should return the actual passage and its context so the reader can assess what it says.\n\nFor a multilingual matching task, keep the purpose of the comparison clear. A candidate pair of translations and a query looking for a supporting passage are different preparations. The model’s role-prefix guidance supplies a concrete convention for those tasks; omitting the prefix is not the documented equivalent because it changes the input pattern used in training.\n\n### Pooling and vector comparison\n\nThe maker’s example averages token representations using an attention mask to exclude padding and then normalises the vector. Normalised dot products give cosine similarity. The 1024-dimensional array represents one prepared text. Keep the exact model and preparation associated with the stored vectors so later queries use the matching representation.\n\nThese vectors can supply candidate rankings or features for a later clustering or classification stage. They do not translate the source or compose an answer. Return the underlying passage text when a retrieval result is meant to support a reader or a later RAG generation stage.\n\n### Training and language coverage\n\nThe model card documents weakly supervised contrastive pretraining followed by supervised fine-tuning. The multilingual preparation includes material such as title–passage pairs, conversational pairs and translation pairs, while labelled retrieval and language-understanding data provide the later stage. This explains the intended text-representation tasks without establishing equal performance in every language.\n\nThe maker explicitly identifies low-resource-language degradation as a limitation. Check the actual languages and wording in the collection, including abbreviations, names and specialised vocabulary. A multilingual label is a useful scope description; the relevance of the returned text still needs to be examined for the audience and task.\n\n### Length and useful results\n\nLong input is truncated to at most 512 tokens. Prepare a lengthy document as coherent passages with the role prefix and relevant headings included. Preserve definitions and qualifications beside the statement they affect, and retain a parent-document identifier and source version outside the vector.\n\nThe manufacturer recommends focusing on relative cosine-score order rather than treating an absolute value as a universal confidence measure. A useful result is a candidate passage set, comparable text representations or features linked to their original records. This exact model remains separate from the English-only E5 v2 checkpoints and from instruction-tuned E5 variants, even when another model also returns 1024 values.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "ab07daa8071d3bde61b0ef019beb275ca594180b2d43e845e3668a7b89597f9b", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "intfloat/multilingual-e5-largeintfloat Multilingual-E5-Large encodes multilingual text into 1024-dimensional vectors with a 24-layer model. It supports the XLM-R hundred-language scope, uses English query/passage prefixes for every language and truncates inputs at 512 tokens.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Multilingual-E5-Large is intfloat’s multilingual dense text embedding model, with twenty-four layers and a 1024-dimensional output. It is initialised from XLM-RoBERTa Large and trained further on multilingual data. The model card describes support for the hundred-language XLM-R scope while noting weaker performance for low-resource languages. Its vectors represent text for retrieval, semantic comparison and numerical-feature tasks.
The trained prefixes are “query:” and “passage:”, including for non-English text. A retrieval query and its corpus passages use their corresponding roles. The prefixes remain those English strings while the source wording retains its own language. For symmetric semantic similarity, paraphrase retrieval, bitext mining or feature use, the maker recommends “query:”.
A practical collection can hold documentation in several supported languages. Retain each passage’s language, original text and source location with its vector. Prepare real search questions from the audience rather than assuming that one English test represents the whole collection. The output should return the actual passage and its context so the reader can assess what it says.
For a multilingual matching task, keep the purpose of the comparison clear. A candidate pair of translations and a query looking for a supporting passage are different preparations. The model’s role-prefix guidance supplies a concrete convention for those tasks; omitting the prefix is not the documented equivalent because it changes the input pattern used in training.
The maker’s example averages token representations using an attention mask to exclude padding and then normalises the vector. Normalised dot products give cosine similarity. The 1024-dimensional array represents one prepared text. Keep the exact model and preparation associated with the stored vectors so later queries use the matching representation.
These vectors can supply candidate rankings or features for a later clustering or classification stage. They do not translate the source or compose an answer. Return the underlying passage text when a retrieval result is meant to support a reader or a later RAG generation stage.
The model card documents weakly supervised contrastive pretraining followed by supervised fine-tuning. The multilingual preparation includes material such as title–passage pairs, conversational pairs and translation pairs, while labelled retrieval and language-understanding data provide the later stage. This explains the intended text-representation tasks without establishing equal performance in every language.
The maker explicitly identifies low-resource-language degradation as a limitation. Check the actual languages and wording in the collection, including abbreviations, names and specialised vocabulary. A multilingual label is a useful scope description; the relevance of the returned text still needs to be examined for the audience and task.
Long input is truncated to at most 512 tokens. Prepare a lengthy document as coherent passages with the role prefix and relevant headings included. Preserve definitions and qualifications beside the statement they affect, and retain a parent-document identifier and source version outside the vector.
The manufacturer recommends focusing on relative cosine-score order rather than treating an absolute value as a universal confidence measure. A useful result is a candidate passage set, comparable text representations or features linked to their original records. This exact model remains separate from the English-only E5 v2 checkpoints and from instruction-tuned E5 variants, even when another model also returns 1024 values.
POST /v1/embeddingstext, token_idsembedding| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits intfloat/multilingual-e5-large | 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 $0.00002 / 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": "intfloat/multilingual-e5-large", "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='intfloat/multilingual-e5-large',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 intfloat/multilingual-e5-large
Use the method paths shown above.