ahmedbesbes

from spacy.matcher import Matcher

nlp = spacy.load("en_core_web_sm")
doc = nlp("Hello my friend!")

pattern = [
   {"TEXT": "Hello"}
]

matcher = Matcher(nlp.vocab)

ahmedbesbes

import spacy

# load a spacy model that detects DNA, RNA and PROTEINS from
# biomedical documents

model = spacy.load(
    "en_ner_jnlpba_md",
    disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"],
)

ahmedbesbes

import spacy

nlp = spacy.blanc("en")
doc_before = nlp("John lives in Atlanta")

# No entities are detected

print(doc_before.ents)
# ()

ahmedbesbes

import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")

doc = nlp("John lives in France and works at Apple Inc.")

print(doc.ents)
# (John, France, Apple Inc.)

ahmedbesbes

import os
import spacy

nlp = spacy.load("en_core_news_sm")

texts = ... # a large list of documents

docs = []

for doc in nlp.pipe(texts, n_process=os.cpu_count()-1):

ahmedbesbes

import streamlit as st

number_of_tabs = st.sidebar.number_input(
    "Number of tabs",
    min_value=1,
    max_value=20,
    value=1,
)

number_of_tabs = int(number_of_tabs)

ahmedbesbes

import numpy as np
import pandas as pd
import streamlit as st

tabs = st.tabs(["metrics", "plots", "reports"])

tab_metrics = tabs[0]

with tab_metrics:
    st.metric("Precision", 0.85, delta=0.2)

ahmedbesbes

import streamlit as st

tabs = st.tabs(["metrics", "plots", "reports"])
tab_plots = tabs[1]

with tab_plots:

    cols = st.columns(2)
    with cols[0]:
        st.image("./roc.png")

ahmedbesbes

import streamlit as st

tabs = st.tabs(["metrics", "plots", "reports"])
tab_plots = tabs[1]

with tab_plots:
    st.image("./roc.png")

ahmedbesbes

import streamlit as st

tabs = st.tabs(["metrics", "plots", "reports"])

tab_metrics = tabs[0]

tab_metrics.metric("Precision", 0.85, delta=0.2)
tab_metrics.metric("Recall", 0.60, delta=-0.1)