News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
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View details ↗Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English. Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale. 1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL. 2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale.
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View details ↗Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement. we proposed KR-FinBert for the financial domain by further pre-training it on a financial corpus and fine-tuning it for sentiment analysis. As many studies have shown, the performance improvement through adaptation and conducting the downstream task was also clear in this experiment. The training data for this model is expanded from those of **KR-BERT-MEDIUM**, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. For the transfer learning, **corporate related economic news articles from 72 media sources** such as the Financial Times, The Korean Economy Daily, etc and **analyst reports from 16 securities companies** such as Kiwoom Securities, Samsung Securities, etc are added. Included in the dataset is 440,067 news titles with their content and 11,237 analyst reports. **The total data size is about 13.22GB.** For mlm training, we split the data line by line and **the total no. of lines is 6,379,315.** KR-FinBert is trained for 5.5M steps with the maxlen of 512, training batch size of 32, and learning rate of 5e-5, taking 67.48 hours to train the model using NVIDIA TITAN XP.
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View details ↗Financial-RoBERTa Financial-RoBERTa is a pre-trained NLP model to analyze sentiment of financial text including: Financial Statements, Earnings Announcements, Earnings Call Transcripts, Corporate Social Responsibility (CSR) Reports, Environmental, Social, and Governance (ESG) News, Financial News, Etc. Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text. The model will give softmax outputs for three labels: Positive , Negative or Neutral . How to perform sentiment analysis: The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:
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View details ↗English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
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View details ↗Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
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