# BERTopic Model for CNN News Articles

This model is a BERTopic model fine-tuned on CNN news articles. It uses the sentence transformer model "all-MiniLM-L6-v2" to encode the sentences and UMAP for dimensionality reduction.

## Usage

First, install the required packages:

```console
pip install sentence_transformers umap-learn bertopic
```

``` python

Then, load the model and encode your documents:

```python
from sentence_transformers import SentenceTransformer
from umap import UMAP
from bertopic import BERTopic

# Load the sentence transformer model
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")

# Set the random state in the UMAP model to prevent stochastic behavior 
umap_model = UMAP(n_neighbors=15, n_components=5,  min_dist=0.0, metric='cosine', random_state=42)

# Load the BERTopic model
my_model = BERTopic.load("from/path/model.bin")

# Encode your documents
document_embeddings = sentence_model.encode(documents)
```


# predict :


```python

sentences = "my sentence"

embeddings = sentence_model.encode([sentences])

topic , _ =my_model.transform([sentences],embeddings)

```


For more information on how to use the BERTopic model, see the (BERTopic documentation)[https://maartengr.github.io/BERTopic/index.html].

{"language": ["en"], "license": "apache-2.0", "tags": ["topic modeling", "BERT", "CNN news articles"], "datasets": ["AyoubChLin/CNN_News_Articles_2011-2022"]}
topic modeling BERT CNN news articles en dataset:AyoubChLin/CNN_News_Articles_2011-2022 license:apache-2.0 region:us
TAGS
#topic modeling #BERT #CNN news articles #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #region-us 

# BERTopic Model for CNN News Articles

This model is a BERTopic model fine-tuned on CNN news articles. It uses the sentence transformer model "all-MiniLM-L6-v2" to encode the sentences and UMAP for dimensionality reduction.

## Usage

First, install the required packages:



python
from sentence_transformers import SentenceTransformer
from umap import UMAP
from bertopic import BERTopic

# Load the sentence transformer model
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")

# Set the random state in the UMAP model to prevent stochastic behavior 
umap_model = UMAP(n_neighbors=15, n_components=5,  min_dist=0.0, metric='cosine', random_state=42)

# Load the BERTopic model
my_model = URL("from/path/URL")

# Encode your documents
document_embeddings = sentence_model.encode(documents)
python

sentences = "my sentence"

embeddings = sentence_model.encode([sentences])

topic , _ =my_model.transform([sentences],embeddings)

'''


For more information on how to use the BERTopic model, see the (BERTopic documentation)[URL

# BERTopic Model for CNN News Articles

This model is a BERTopic model fine-tuned on CNN news articles. It uses the sentence transformer model "all-MiniLM-L6-v2" to encode the sentences and UMAP for dimensionality reduction. ## Usage

First, install the required packages:



python
from sentence_transformers import SentenceTransformer
from umap import UMAP
from bertopic import BERTopic # Load the sentence transformer model
sentence_model = SentenceTransformer("all-MiniLM-L6-v2") # Set the random state in the UMAP model to prevent stochastic behavior 
umap_model = UMAP(n_neighbors=15, n_components=5,  min_dist=0.0, metric='cosine', random_state=42) # Load the BERTopic model
my_model = URL("from/path/URL") # Encode your documents
document_embeddings = sentence_model.encode(documents)
python

sentences = "my sentence"

embeddings = sentence_model.encode([sentences])

topic , _ =my_model.transform([sentences],embeddings)

'''


For more information on how to use the BERTopic model, see the (BERTopic documentation)[URL
TAGS
#topic modeling #BERT #CNN news articles #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #region-us 
 # BERTopic Model for CNN News Articles

This model is a BERTopic model fine-tuned on CNN news articles. It uses the sentence transformer model "all-MiniLM-L6-v2" to encode the sentences and UMAP for dimensionality reduction. ## Usage

First, install the required packages:



python
from sentence_transformers import SentenceTransformer
from umap import UMAP
from bertopic import BERTopic # Load the sentence transformer model
sentence_model = SentenceTransformer("all-MiniLM-L6-v2") # Set the random state in the UMAP model to prevent stochastic behavior 
umap_model = UMAP(n_neighbors=15, n_components=5,  min_dist=0.0, metric='cosine', random_state=42) # Load the BERTopic model
my_model = URL("from/path/URL") # Encode your documents
document_embeddings = sentence_model.encode(documents)
python

sentences = "my sentence"

embeddings = sentence_model.encode([sentences])

topic , _ =my_model.transform([sentences],embeddings)

'''


For more information on how to use the BERTopic model, see the (BERTopic documentation)[URL
TAGS
#topic modeling #BERT #CNN news articles #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #region-us 
# BERTopic Model for CNN News Articles

This model is a BERTopic model fine-tuned on CNN news articles. It uses the sentence transformer model "all-MiniLM-L6-v2" to encode the sentences and UMAP for dimensionality reduction.## Usage

First, install the required packages:



python
from sentence_transformers import SentenceTransformer
from umap import UMAP
from bertopic import BERTopic# Load the sentence transformer model
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")# Set the random state in the UMAP model to prevent stochastic behavior 
umap_model = UMAP(n_neighbors=15, n_components=5,  min_dist=0.0, metric='cosine', random_state=42)# Load the BERTopic model
my_model = URL("from/path/URL")# Encode your documents
document_embeddings = sentence_model.encode(documents)
python

sentences = "my sentence"

embeddings = sentence_model.encode([sentences])

topic , _ =my_model.transform([sentences],embeddings)

'''


For more information on how to use the BERTopic model, see the (BERTopic documentation)[URL