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import spaces
import gradio as gr
from numpy.linalg import norm
from transformers import AutoModel
from sentence_transformers import SentenceTransformer
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model1 = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-code", trust_remote_code=True)
model2 = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-en", trust_remote_code=True)
model3 = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-zh", trust_remote_code=True)
model4 = SentenceTransformer("aspire/acge_text_embedding")
model5 = SentenceTransformer("intfloat/multilingual-e5-large")
@spaces.GPU
def generate(input1, input2):
if len(input1) < 1:
input1 = "How do I access the index while iterating over a sequence with a for loop?"
if len(input2) < 1:
input2 = "# Use the built-in enumerator\nfor idx, x in enumerate(xs):\n print(idx, x)"
embeddings1 = model1.encode(
[
input1,
input2,
]
)
score1 = cos_sim(embeddings1[0], embeddings1[1])
embeddings2 = model2.encode(
[
input1,
input2,
]
)
score2 = cos_sim(embeddings2[0], embeddings2[1])
embeddings3 = model3.encode(
[
input1,
input2,
]
)
score3 = cos_sim(embeddings3[0], embeddings3[1])
embeddings4 = model4.encode(
[
input1,
input2,
]
)
score4 = cos_sim(embeddings4[0], embeddings4[1])
embeddings5 = model5.encode(
[
input1,
input2,
]
)
score5 = cos_sim(embeddings5[0], embeddings5[1])
return score1, score2, score3, score4, score5
gr.Interface(
fn=generate,
inputs=[
gr.Text(label="input1", placeholder="How do I access the index while iterating over a sequence with a for loop?"),
gr.Text(label="input2", placeholder="# Use the built-in enumerator\nfor idx, x in enumerate(xs):\n print(idx, x)"),
],
outputs=[
gr.Text(label="jinaai/jina-embeddings-v2-base-code"),
gr.Text(label="jinaai/jina-embeddings-v2-base-en"),
gr.Text(label="jinaai/jina-embeddings-v2-base-zh"),
gr.Text(label="aspire/acge_text_embedding"),
gr.Text(label="intfloat/multilingual-e5-large"),
],
).launch() |