evaluation-metrics
- A Package for Machine Learning Evaluation Reporting (16 Nov 2024)
- Benchmarking Haystack Pipelines for Optimal Performance (24 Jun 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. - Named-Entity evaluation metrics based on entity-level (09 May 2018)
Named-Entity evaluation metrics based on entity-level
retrieval-augmented-generation
Haystack
viterbi
sequence-prediction
evaluation-metrics
scikit-learn
pos-tags
named-entity-recognition
embeddings
conditional-random-fields
classification
word2vec
triplet-loss
syntactic-dependencies
sentence-transformers
relationship-extraction
neural-networks
information-retrieval
fine-tuning
coursera
conference
SyntaxNet
NLTK
LSTM
wikidata
transformers
tokenization
tf-idf
text-summarisation
semantic-web
semantic-drift
resources
reference-post
production
portuguese
political-science
naive-bayes
multi-label-classification
monitoring
mlops
metadata-extraction
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