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gensim_example.py
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gensim_example.py
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# Copyright 2023 Neal Lathia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from gensim.models import word2vec
from libraries.util.datasets import load_text_dataset
from libraries.util.domains import NEWSGROUP_EMBEDDINGS_DOMAIN
from modelstore.model_store import ModelStore
def _train_example_model() -> word2vec.Word2Vec:
# Load the data
sentences = load_text_dataset()
# Train a word2vec model
print(f"🤖 Training a word2vec model...")
model = word2vec.Word2Vec(sentences, min_count=2)
most_similar = set([k[0] for k in model.wv.most_similar("cool", topn=5)])
print(f"🤖 Most similar to 'cool': {most_similar}")
return model
def train_and_upload(modelstore: ModelStore) -> dict:
# Train a word2vec model
model = _train_example_model()
# Upload the model to the model store
print(
f"⤴️ Uploading the word2vec model to the {NEWSGROUP_EMBEDDINGS_DOMAIN} domain."
)
meta_data = modelstore.upload(NEWSGROUP_EMBEDDINGS_DOMAIN, model=model)
return meta_data
def load_and_test(modelstore: ModelStore, model_domain: str, model_id: str):
# Load the model back into memory!
print(f'⤵️ Loading the word2vec "{model_domain}" domain model={model_id}')
model = modelstore.load(model_domain, model_id)
# Find some nearest neighbours
most_similar = set([k[0] for k in model.wv.most_similar("cool", topn=5)])
print(f"🤖 Most similar to 'cool': {most_similar}")