The distilbert-base-nli-mean-tokens model is the DistilBERT base model for natural language inference (NLI) using the MEAN pooling strategy for CLS tokens. For more information on the DistilBERT base model, have a look at the original paper. The pooling strategy is simply the way the model combines the different embeddings (and thus information) from the ...
Doc2vec and similar algorithms are useful methods to create document embeddings.
You should try to include as much data and metadata as possible.
The size of the documents does not matter much because they will be project into a fixed dimensional embedding space.
As far as noise, the effect will be task specific. If you are doing similarity analysis and ...
You did not mention which package you are using. If you using scikit-learn,
sklearn.pipeline.FeatureUnion concatenates results of multiple transformer objects.
Something like this:
from sklearn.cluster import DBSCAN
from sklearn.pipeline import FeatureUnion, Pipeline
from skearnsklearn.preprocessing import StandardScaler
pipeline = ...
Amirhossein's blog post explains the intuition for positional encoding very well.
My takeaway from the blog is that: Consider just a pair of sinusoids (sine and cosine). Suppose we are within 1 full cycle (e.g. 0 to 2pi), the resulting encoding is basically guaranteed to be unique. I.e. there is a 1-to-1 mapping from real numbers (1, 1.5, 2, 2.34,etc.), x, ...
Nlp like spacy remove stop works and identify proper nouns and nouns. It does fairly well at identifying nouns but is not perfect. Try a sample of your data and see how accurate your percentages become. Use the medium model
There's a bit of confusion about your task: first, apparently by "emails" you mean email addresses, not the full content of the emails, right?
What you're doing looks like record linkage. The complexity of comparing every pair of records (here email addresses) can be decreased using the technique called blocking, where roughly similar pairs are ...