jina-reranker-m0-GGUF

Name: jina-reranker-m0-GGUF
Author: jinaai

860

license:cc-by-nc-4.0

jinaai

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OTHER

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860 downloads

Early-stage

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Quick Summary

jina-reranker-m0-gguf `jina-reranker-m0` is a cutting-edge multimodal, multilingual reranker for text, code, image and visual document reranking.

Code Examples

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

python

import numpy as np
with np.load('mlp_weights.npz') as data:
    W1, b1, W2, b2 = data['W1'], data['b1'], data['W2'], data['b2']
    logit_bias = float(data['logit_bias'][0])

mlp = lambda x: 1 / (1 + np.exp(-((np.maximum(0, x @ W1 + b1) @ W2 + b2) - logit_bias)))

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