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Anyone know what Bert is?
Coin Finder
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@Catbert
@Coinbert
Maybe these guys know?
Superb collections are built more by omission than by addition.
They are a what knot seller (trader bea) and that is their special label they get.
Positive BST as a seller: Namvet69, Lordmarcovan, Bigjpst, Soldi, mustanggt, CoinHoader, moursund, SufinxHi, al410, JWP
I think there’s a much better chance that Ernie knows.
Mark Feld* of Heritage Auctions*Unless otherwise noted, my posts here represent my personal opinions.
Apparently BERT is a sticker made by the seller TraderBea, who sells on whatnot.
https://traderbea.com/bertchase/
Prior version:
https://forums.collectors.com/discussion/1106320/anyone-know-what-these-pcgs-trader-bea-holders-are-promotional-item-dealer-bulk-sub
Seems like their marketing is working given how many threads there have been about it here.
Superb collections are built more by omission than by addition.
Sorry. Only know BART:Bay Area Rapid Transit.
BERT is a language model developed by Google that uses a transformer architecture to understand the context of words in a sentence from both directions, left-to-right and right-to-left. It is pre-trained on a massive amount of text data and can then be fine-tuned for various specific tasks with minimal effort. This makes it highly effective for applications like improved search queries, sentiment analysis, and question answering.
Key characteristics
Bidirectional training: Unlike older models that processed text in a single direction, BERT analyzes words in the context of the entire sentence, from both the left and the right.
Pre-training and fine-tuning: BERT is first pre-trained on general language patterns. Developers can then "fine-tune" it with a small amount of task-specific data to make it perform well on new tasks like sentiment analysis or question answering.
Transformer architecture: It is built on the transformer model, which uses a mechanism called "self-attention" to weigh the importance of different words in a sentence.
Auto-encoding: It uses a type of auto-encoding language model to create a vector representation for each word based on its contextual information.
Common applications
Search engines: It helps search engines understand the context of user queries, leading to more accurate results.
Question answering: It can find the specific span of text that answers a question within a larger document.
Sentiment analysis: It can determine the emotional tone (positive, negative, or neutral) of text, such as customer reviews.
Text classification: It can categorize text into different groups.
Named Entity Recognition (NER): It identifies and categorizes entities like names of people, organizations, and locations within a text.
Chatbots and virtual assistants: It enables more natural and context-aware conversations.
Proud follower of Christ! I love the USA! Land of the Bright and Beautiful! 🇺🇸🇺🇸🇺🇸🇺🇸🇺🇸
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"The BERT label feature a
variety of designs
& themes to enhance
the presentation
of different
coins and sets."
A confusing thread for a coin site. I assumed Bert meant the inner letters of LI BERT Y. James
Bert was a great hobo nickel carver
They spelled "BURT" wrong..........
"When they can't find anything wrong with you, they create it!"