File size: 8,720 Bytes
6b63571 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 |
import streamlit as st
# Page configuration
st.set_page_config(
layout="wide",
initial_sidebar_state="auto"
)
# Custom CSS for better styling
st.markdown("""
<style>
.main-title {
font-size: 36px;
color: #4A90E2;
font-weight: bold;
text-align: center;
}
.sub-title {
font-size: 24px;
color: #4A90E2;
margin-top: 20px;
}
.section {
background-color: #f9f9f9;
padding: 15px;
border-radius: 10px;
margin-top: 20px;
}
.section h2 {
font-size: 22px;
color: #4A90E2;
}
.section p, .section ul {
color: #666666;
}
.link {
color: #4A90E2;
text-decoration: none;
}
.benchmark-table {
width: 100%;
border-collapse: collapse;
margin-top: 20px;
}
.benchmark-table th, .benchmark-table td {
border: 1px solid #ddd;
padding: 8px;
text-align: left;
}
.benchmark-table th {
background-color: #4A90E2;
color: white;
}
.benchmark-table td {
background-color: #f2f2f2;
}
</style>
""", unsafe_allow_html=True)
# Title
st.markdown('<div class="main-title">Introduction to CamemBERT Annotators in Spark NLP</div>', unsafe_allow_html=True)
# Subtitle
st.markdown("""
<div class="section">
<p>Spark NLP offers a variety of CamemBERT-based annotators tailored for multiple natural language processing tasks. CamemBERT is a robust and versatile model designed specifically for the French language, offering state-of-the-art performance in a range of NLP applications. Below, we provide an overview of the four key CamemBERT annotators:</p>
</div>
""", unsafe_allow_html=True)
st.markdown("""
<div class="section">
<h2>CamemBERT for Token Classification</h2>
<p>The <strong>CamemBertForTokenClassification</strong> annotator is designed for Named Entity Recognition (NER) tasks using CamemBERT, a French language model derived from RoBERTa. This model efficiently handles token classification, which involves labeling tokens in a text with tags that correspond to specific entities. CamemBERT offers robust performance in French NLP tasks, making it a valuable tool for real-time applications in this language.</p>
<p>Token classification with CamemBERT enables:</p>
<ul>
<li><strong>Named Entity Recognition (NER):</strong> Identifying and classifying entities such as names, organizations, locations, and other predefined categories.</li>
<li><strong>Information Extraction:</strong> Extracting key information from unstructured text for further analysis.</li>
<li><strong>Text Categorization:</strong> Enhancing document retrieval and categorization based on entity recognition.</li>
</ul>
<p>Here is an example of how CamemBERT token classification works:</p>
<table class="benchmark-table">
<tr>
<th>Entity</th>
<th>Label</th>
</tr>
<tr>
<td>Paris</td>
<td>LOC</td>
</tr>
<tr>
<td>Emmanuel Macron</td>
<td>PER</td>
</tr>
<tr>
<td>Élysée Palace</td>
<td>ORG</td>
</tr>
</table>
</div>
""", unsafe_allow_html=True)
# CamemBERT Token Classification - French WikiNER
st.markdown('<div class="sub-title">CamemBERT Token Classification - French WikiNER</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<p>The <strong>camembert_base_token_classifier_wikiner</strong> is a fine-tuned CamemBERT model for token classification tasks, specifically adapted for Named Entity Recognition (NER) on the French WikiNER dataset. It is designed to recognize five types of entities: O, LOC, PER, MISC, and ORG.</p>
</div>
""", unsafe_allow_html=True)
# How to Use the Model - Token Classification
st.markdown('<div class="sub-title">How to Use the Model</div>', unsafe_allow_html=True)
st.code('''
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline
from pyspark.sql.functions import col, expr
document_assembler = DocumentAssembler() \\
.setInputCol('text') \\
.setOutputCol('document')
tokenizer = Tokenizer() \\
.setInputCols(['document']) \\
.setOutputCol('token')
tokenClassifier = CamemBertForTokenClassification \\
.pretrained('camembert_base_token_classifier_wikiner', 'en') \\
.setInputCols(['document', 'token']) \\
.setOutputCol('ner') \\
.setCaseSensitive(True) \\
.setMaxSentenceLength(512)
# Convert NER labels to entities
ner_converter = NerConverter() \\
.setInputCols(['document', 'token', 'ner']) \\
.setOutputCol('entities')
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
tokenClassifier,
ner_converter
])
data = spark.createDataFrame([["""Paris est la capitale de la France et abrite le Président Emmanuel Macron, qui réside au palais de l'Élysée. Apple Inc. a une présence significative dans la ville."""]]).toDF("text")
result = pipeline.fit(data).transform(data)
result.select(
expr("explode(entities) as ner_chunk")
).select(
col("ner_chunk.result").alias("chunk"),
col("ner_chunk.metadata.entity").alias("ner_label")
).show(truncate=False)
''', language='python')
# Results
st.text("""
+------------------+---------+
|chunk |ner_label|
+------------------+---------+
|Paris |LOC |
|France |LOC |
|Emmanuel Macron |PER |
|Élysée Palace |ORG |
|Apple Inc. |ORG |
+------------------+---------+
""")
# Performance Metrics
st.markdown('<div class="sub-title">Performance Metrics</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<p>Here are the detailed performance metrics for the CamemBERT token classification model:</p>
<table class="benchmark-table">
<tr>
<th>Entity</th>
<th>Precision</th>
<th>Recall</th>
<th>F1-Score</th>
</tr>
<tr>
<td>LOC</td>
<td>0.93</td>
<td>0.94</td>
<td>0.94</td>
</tr>
<tr>
<td>PER</td>
<td>0.95</td>
<td>0.95</td>
<td>0.95</td>
</tr>
<tr>
<td>ORG</td>
<td>0.92</td>
<td>0.91</td>
<td>0.91</td>
</tr>
<tr>
<td>MISC</td>
<td>0.86</td>
<td>0.85</td>
<td>0.85</td>
</tr>
<tr>
<td>O</td>
<td>0.99</td>
<td>0.99</td>
<td>0.99</td>
</tr>
<tr>
<td>Overall</td>
<td>0.97</td>
<td>0.98</td>
<td>0.98</td>
</tr>
</table>
</div>
""", unsafe_allow_html=True)
# Model Information - Token Classification
st.markdown('<div class="sub-title">Model Information</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<ul>
<li><strong>Model Name:</strong> camembert_base_token_classifier_wikiner</li>
<li><strong>Compatibility:</strong> Spark NLP 4.2.0+</li>
<li><strong>License:</strong> Open Source</li>
<li><strong>Edition:</strong> Official</li>
<li><strong>Input Labels:</strong> [token, document]</li>
<li><strong>Output Labels:</strong> [ner]</li>
<li><strong>Language:</strong> French</li>
<li><strong>Size:</strong> 412.2 MB</li>
<li><strong>Case Sensitive:</strong> Yes</li>
<li><strong>Max Sentence Length:</strong> 512</li>
</ul>
</div>
""", unsafe_allow_html=True)
# References - Token Classification
st.markdown('<div class="sub-title">References</div>', unsafe_allow_html=True)
st.markdown("""
<div class="section">
<ul>
<li><a class="link" href="https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr" target="_blank" rel="noopener">CamemBERT WikiNER Dataset</a></li>
<li><a class="link" href="https://sparknlp.org/2022/09/23/camembert_base_token_classifier_wikiner_en.html" target="_blank" rel="noopener">CamemBERT Token Classification on Spark NLP Hub</a></li>
</ul>
</div>
""", unsafe_allow_html=True)
|