Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-large-instruct") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use intfloat/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/multilingual-e5-large-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large-instruct") model = AutoModel.from_pretrained("intfloat/multilingual-e5-large-instruct") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 690 Bytes
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"_name_or_path": "tmp/",
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float16",
"transformers_version": "4.36.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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