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Tiiiger avatar

Tiiiger/bert_score

0
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1,903 stars·240 forks·Jupyter Notebook·MIT·13 views

Bert Score

BERT Score is a text evaluation tool that assesses the quality of generated text by computing precision, recall, and F1 metrics between candidate and reference texts. It transforms text tokens into dense contextual vectors using pretrained transformer models, calculating token-level similarity matrices through pairwise cosine distances. The system computes scores by greedily matching tokens between sequences and supports multilingual assessment across dozens of different languages by utilizing language-specific or cross-lingual transformer backends.

The library includes features for baseline score rescaling to widen the output score range for easier interpretation, along with persistent model caching to retain loaded neural network weights in active memory across multiple runs. Additionally, it provides HTML-based visualization capabilities that generate interactive token alignment diagrams showing how words match between candidate and reference sentences.

Features

  • Quality Evaluation Frameworks - Measures the quality of generated text against reference sentences using precision, recall, and F1 metrics derived from contextual embeddings.
  • BERT Score Tooling - Provides a metric for text generation that computes token similarity using contextual embeddings to measure precision, recall, and F1.
  • Generation Quality Evaluations - Computes token similarity scores using contextual embeddings from transformer models to measure precision, recall, and F1.
  • Text Evaluations - Computes similarity metrics across dozens of different human languages using cross-lingual transformer backends.
  • Text Generation Metrics - Analyzes machine-translated or summarized text by aligning tokens between candidate and reference sentences.
  • Contextual Embeddings - Transforms text tokens into dense contextual vectors using pretrained transformer models.
  • Greedy Maximum Matchings - Computes precision and recall scores by greedily matching each token in one sequence to its most similar counterpart.
  • Cosine Similarity Alignments - Calculates token-level similarity matrices by computing pairwise cosine distances between candidate and reference embedding vectors.
  • Cross-Lingual Model Routing - Selects language-specific or multilingual transformer backends dynamically based on specified language codes.
  • Multilingual Evaluation Tooling - Computes similarity metrics across dozens of different languages using multilingual or language-specific transformer models.
  • Text Similarity Scoring - Computes semantic similarity and evaluation scores across dozens of different human languages.

Star history

Star history chart for tiiiger/bert_scoreStar history chart for tiiiger/bert_score

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does tiiiger/bert_score do?

BERT Score is a text evaluation tool that assesses the quality of generated text by computing precision, recall, and F1 metrics between candidate and reference texts. It transforms text tokens into dense contextual vectors using pretrained transformer models, calculating token-level similarity matrices through pairwise cosine distances. The system computes scores by greedily matching tokens between sequences and supports multilingual assessment across dozens of different…

What are the main features of tiiiger/bert_score?

The main features of tiiiger/bert_score are: Quality Evaluation Frameworks, BERT Score Tooling, Generation Quality Evaluations, Text Evaluations, Text Generation Metrics, Contextual Embeddings, Greedy Maximum Matchings, Cosine Similarity Alignments.

What are some open-source alternatives to tiiiger/bert_score?

Open-source alternatives to tiiiger/bert_score include: stanford-futuredata/colbert — ColBERT is a neural information retrieval model and dense passage retrieval framework. It functions as a search engine… ymcui/chinese-bert-wwm — Chinese-BERT-wwm is a pre-trained transformer model and encoder designed for Chinese natural language processing. It… milanlproc/contextualized-topic-models — Contextualized topic modeling is a framework that integrates deep learning architectures with statistical word… flairnlp/flair — Flair is a transformer-based natural language processing framework used to build and train models for text… datawhalechina/so-large-lm — This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of… rapidfuzz/rapidfuzz — RapidFuzz is a C++ accelerated Python library providing high-performance string comparison and similarity…

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