Sentence Similarity
Transformers
ONNX
Safetensors
sentence-transformers
Transformers.js
English
new
feature-extraction
gte
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-base-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/gte-base-en-v1.5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use Alibaba-NLP/gte-base-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers.js
How to use Alibaba-NLP/gte-base-en-v1.5 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Alibaba-NLP/gte-base-en-v1.5'); - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "NewModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", | |
| "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", | |
| "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM", | |
| "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice", | |
| "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering", | |
| "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification", | |
| "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification" | |
| }, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "layer_norm_type": "layer_norm", | |
| "max_position_embeddings": 8192, | |
| "model_type": "new", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pack_qkv": true, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "rope", | |
| "rope_scaling": { | |
| "factor": 2.0, | |
| "type": "ntk" | |
| }, | |
| "rope_theta": 500000, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.1", | |
| "type_vocab_size": 0, | |
| "unpad_inputs": false, | |
| "use_memory_efficient_attention": false, | |
| "vocab_size": 30528 | |
| } | |