Sponsored by APIMart
Toolify Toolify logo
AI Models
bge-base-en-v1-5-seqlen-384-bs-1

aws-neuron / bge-base-en-v1-5-seqlen-384-bs-1

Run on huggingface.co
huggingface.co
Total runs: 51
24-hour runs: -3
7-day runs: -24
30-day runs: -79
Model's Last Updated: November 21 2023
feature-extraction

Introduction of bge-base-en-v1-5-seqlen-384-bs-1

Model Details of bge-base-en-v1-5-seqlen-384-bs-1

Neuronx model for BAAI/bge-base-en-v1.5

This repository contains are AWS Inferentia2 and neuronx compatible checkpoint for BAAI/bge-base-en-v1.5 . You can find detailed information about the base model on its Model Card .

Usage on Amazon SageMaker

coming soon

Usage with optimum-neuron 

from optimum.neuron import NeuronModelForFeatureExtraction
from transformers import AutoTokenizer
import torch
import torch_neuronx

# Load Model from Hugging Face repository
model = NeuronModelForFeatureExtraction.from_pretrained("aws-neuron/bge-base-en-v1-5-seqlen-384-bs-1")
tokenizer = AutoTokenizer.from_pretrained("aws-neuron/bge-base-en-v1-5-seqlen-384-bs-1")

# sentence input
inputs = "Hello, my dog is cute"

# Tokenize sentences
encoded_input = tokenizer(inputs,return_tensors="pt",truncation=True,max_length=model.config.neuron["static_sequence_length"])

# Compute embeddings
with torch.no_grad():
    model_output = model(*tuple(encoded_input.values()))

# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)

input_shapes 

{
  "sequence_length": 384,
  "batch_size": 1
}

Runs of aws-neuron bge-base-en-v1-5-seqlen-384-bs-1 on huggingface.co

51
Total runs
-3
24-hour runs
-8
3-day runs
-24
7-day runs
-79
30-day runs

More Information About bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co Model

More bge-base-en-v1-5-seqlen-384-bs-1 license Visit here:

https://choosealicense.com/licenses/mit

bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co

bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co is an AI model on huggingface.co that provides bge-base-en-v1-5-seqlen-384-bs-1's model effect (), which can be used instantly with this aws-neuron bge-base-en-v1-5-seqlen-384-bs-1 model. huggingface.co supports a free trial of the bge-base-en-v1-5-seqlen-384-bs-1 model, and also provides paid use of the bge-base-en-v1-5-seqlen-384-bs-1. Support call bge-base-en-v1-5-seqlen-384-bs-1 model through api, including Node.js, Python, http.

bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co Url

https://huggingface.co/aws-neuron/bge-base-en-v1-5-seqlen-384-bs-1

aws-neuron bge-base-en-v1-5-seqlen-384-bs-1 online free

bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co is an online trial and call api platform, which integrates bge-base-en-v1-5-seqlen-384-bs-1's modeling effects, including api services, and provides a free online trial of bge-base-en-v1-5-seqlen-384-bs-1, you can try bge-base-en-v1-5-seqlen-384-bs-1 online for free by clicking the link below.

aws-neuron bge-base-en-v1-5-seqlen-384-bs-1 online free url in huggingface.co:

https://huggingface.co/aws-neuron/bge-base-en-v1-5-seqlen-384-bs-1

bge-base-en-v1-5-seqlen-384-bs-1 install

bge-base-en-v1-5-seqlen-384-bs-1 is an open source model from GitHub that offers a free installation service, and any user can find bge-base-en-v1-5-seqlen-384-bs-1 on GitHub to install. At the same time, huggingface.co provides the effect of bge-base-en-v1-5-seqlen-384-bs-1 install, users can directly use bge-base-en-v1-5-seqlen-384-bs-1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

bge-base-en-v1-5-seqlen-384-bs-1 install url in huggingface.co:

https://huggingface.co/aws-neuron/bge-base-en-v1-5-seqlen-384-bs-1

Url of bge-base-en-v1-5-seqlen-384-bs-1

bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co Url

Provider of bge-base-en-v1-5-seqlen-384-bs-1 huggingface.co

aws-neuron
ORGANIZATIONS
huggingface.co

Other API from aws-neuron