ctc-gen-eval
COMET
| ctc-gen-eval | COMET | |
|---|---|---|
| 3 | 3 | |
| 93 | 418 | |
| - | 4.3% | |
| 1.3 | 7.7 | |
| about 1 year ago | about 1 month ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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ctc-gen-eval
COMET
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Benchmarking of OpenAI GPT-3 VS other proprietary APIs (details in dev.to/samyme article)
It's definitely a hard task to evaluate. I think we can use models like https://github.com/Unbabel/COMET for translation to try and mimic human evaluation. I don't know if datasets exist for that. There are some research done about that : https://aclanthology.org/P19-1502/ https://arxiv.org/abs/2104.00054v1
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OpenAI GPT-3 vs Other Models [Benchmark] - Should AI companies be really worried ?
2/ Evaluation We compare Open AI to DeepL, ModernMT, NeuralSpace, Amazon and Google. A lot of metrics exist for automatic machine translation evaluation. We chose COMET by Unbabel (wmt21-comet-da) which is based on a machine learning model trained to get state-of-the-art levels of correlation with human judgements. (read more on their paper ) .
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What does the output of COMET metric really mean ?
I'm trying to understand how I can use COMET to evaluate translation models https://github.com/Unbabel/COMET ? I don't really understand how it was trained the meaning of the outputed values ? https://unbabel.github.io/COMET/html/faqs.html#which-comet-model-should-i-use
What are some alternatives?
edenai-apis - Eden AI: simplify the use and deployment of AI technologies by providing a unique API that connects to the best possible AI engines
Tatoeba-Challenge
image-similarity-measures - :chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
AutomaticKeyphraseExtraction - Data for Automatic Keyphrase Extraction Task
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python