Now for some high-resource languages,
Greek @4re3d0m
's fine-tuned Whisper large checkpoint gets an impressive 10.14 WER when compared to 16 (large) Model: https://
huggingface.co/emilios/whispe
r-large-v2-el-c2
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OPEN SOURCE
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Fine-tuned Whisper Model Achieves Impressive Greek Speech Recognition
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Fine-tuned Whisper Model Achieves 17.8 WER for Latvian
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Latvian @anuragcomm
's fine-tuned Whisper large checkpoint gets an impressive WER of 17.8 when compared to 25.5 by the large checkpoint! Model: -
Whisper Small Model Achieves 8.56 WER for Catalan Speech
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Catalan @jordimash
's fine-tuned Whisper small checkpoint produced an astounding WER of 8.56 compared to 23.8 by the large checkpoint! Model: https://
huggingface.co/softcatala/whi
sper-small-ca
… Space: https://
huggingface.co/spaces/softcat
ala/whisper-demo-catalan
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Fine-tuned Whisper Model Achieves Significant WER Improvement for Kannada
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Kannada @saiteya10294's fine-tuned Whisper small checkpoint managed to get the WER down from 100.4 (large) -> 23.1! Model: https://
huggingface.co/steja/whisper-
small-kannada
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Lithuanian Whisper Model Achieves Major WER Improvement
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Lithuanian @DeividasMat
's fine-tuned Whisper medium checkpoint managed to get the WER down from 35.2 (large) -> 20.44 Model: -
Researchers Win OGB-LSC Competition with Accelerated GNN Training
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Super proud of our researchers for winning the MAG240M Track of the OGB-LSC competition at #NeurIPS2022! They used PGL and PaddlePaddle to accelerate GNN training on GPUs, reaching SOTA in only 1 hr (40 hrs before). Check out their code: https://
github.com/PaddlePaddle/P
GL/tree/main/examples/NeurIPS2022-OGB-Challenge/MAG240M
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Try Replicate’s Inpainter Tool on GitHub
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Try it out yourself: https://t.co/d6dnzXsjR5 https://t.co/6X4w43bgJ1
— Replicate (@replicate) 15 décembre 2022Try it out yourself: https://
github.com/replicate/inpa
inter
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ERNIE-Code: Unified Multilingual Code-to-Text Model
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Introducing ERNIE-Code, a unified pre-trained language model for 116 natural languages and 6 programming languages. ERNIE-Code outperforms previous models for multilingual code-to-text, text-to-code, code-to-code, and text-to-text generation. Paper: https://
arxiv.org/abs/2212.06742 -
LangChain Enables LLM Caching with Three Lines of Code
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Caching now enabled With 3 lines of code, you can now enable caching for all LLM calls This makes it cheaper and easier to experiment with changing only parts of a chain Supports both a temporary InMemoryCache, as well as a persistent SQLiteCache
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LangChain v0.0.38: New LLM Caching Feature Released
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New in v0.0.38 Read more about this here: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/llm_functionality.html#Caching
… @AkashSamant4 for making LLM configurations serializable, a prerequisite for this @brucehammer and @DannyHabibs for highlighting this as a valuable feature