classification
- A Package for Machine Learning Evaluation Reporting (16 Nov 2024)
- Evaluation Metrics, ROC-Curves and imbalanced datasets (19 Aug 2018)
This blog post describes some evaluation metrics used in NLP, it points out where we should use each one of them and the advantages and disadvantages of each.
viterbi
sequence-prediction
scikit-learn
pos-tags
evaluation_metrics
conditional-random-fields
NER
word2vec
word-embeddings
triplet-loss
syntactic-dependencies
sentence-transformers
relationship-extraction
neural-networks
fine-tuning
embeddings
coursera
conference
classification
SyntaxNet
NLTK
LSTM
CRF
wikidata
transformers
tokenization
tf-idf
text-summarisation
semantic-web
resources
reference-post
production
portuguese
political-science
named-entity-recognition
naive-bayes
multi-label-classification
monitoring
mlops
metrics
maximum-entropy-markov-models
logistic-regression
llms
language-models
information-extraction
imbalanced_data
hyperparameter-optimization
hidden-markov-models
grid-search
gensim
generative-ai
fasttext
document-classification
doc2vec
deployment
dependency-graph
dataset
data-challenge
convolutional-neural-networks
contrastive-learning
books
attention
SPARQL
RNN
PyData
KOVENS
GRU