pubmedbert-base-embeddings-8M

Name: pubmedbert-base-embeddings-8M
Author: NeuML

license:apache-2.0

NeuML

Embedding Model

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28 downloads

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Quick Summary

AI model with specialized capabilities.

Code Examples

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Run a querypython

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding

# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-8M")
model = SentenceTransformer(modules=[static])

sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

Usage (Model2Vec)python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-8M")

# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)

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