You will have to send one page at a time, most of this work has to be done via RAG. Adding a large context (like a whole PDF), still does not work that well in my experience.
123 karma · joined June 16, 2021
HoloViews hvPlot Datashader Plotly Bokeh Seaborn Panel PyDeck cuxfilter node RAPIDS
introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream evaluation areas and achieves competitive performance to the leading open models in the remaining ones. Specifically, Nemotron-4 15B exhibits the best multilingual capabilities of all similarly-sized models, even outperforming models over four times larger and those explicitly specialized for multilingual tasks.
Checkout the docs at: https://maartengr.github.io/BERTopic/faq.html#can-i-use-the-...
All you need to do is below
from bertopic import BERTopic
from cuml.cluster import HDBSCAN
from cuml.manifold import UMAP
# Create instances of GPU-accelerated UMAP and HDBSCAN
umap_model = UMAP(n_components=5, n_neighbors=15, min_dist=0.0)
hdbscan_model = HDBSCAN(min_samples=10, gen_min_span_tree=True)
# Pass the above models to be used in BERTopic
topic_model = BERTopic(umap_model=umap_model, hdbscan_model=hdbscan_model)
topics, probs = topic_model.fit_transform(docs)See dask-sql: https://dask-sql.readthedocs.io/en/latest/pages/api.html