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README.md
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---
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language:
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license: bigscience-bloom-rail-1.0
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datasets:
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- bookcorpus
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- wikipedia
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pipeline_tag: token-classification
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widget:
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---
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# LinkBERT: Fine-tuned BERT for Natural Link Prediction
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- 16 attention heads
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- 336M parameters.
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<blockquote class="twitter-tweet" data-media-max-width="560"><p lang="en" dir="ltr">Here's how we evaluated the quality of LinkBERT's predictions. <a href="https://t.co/n5D9asYxex">pic.twitter.com/n5D9asYxex</a></p>— DEJAN (@dejanseo) <a href="https://twitter.com/dejanseo/status/1768497786205650948?ref_src=twsrc%5Etfw">March 15, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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language:
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- en
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- fr
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- es
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- de
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- it
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- ko
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- ar
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- hi
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- id
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- ru
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- uk
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- pl
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license: bigscience-bloom-rail-1.0
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datasets:
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- bookcorpus
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- wikipedia
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pipeline_tag: token-classification
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widget:
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LinkBERT is an advanced fine-tuned version of the bert-large-cased model
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developed by Dejan Marketing. The model is designed to predict natural link
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placement within web content.
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---
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# LinkBERT: Fine-tuned BERT for Natural Link Prediction
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- 16 attention heads
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- 336M parameters.
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<blockquote class="twitter-tweet" data-media-max-width="560"><p lang="en" dir="ltr">Here's how we evaluated the quality of LinkBERT's predictions. <a href="https://t.co/n5D9asYxex">pic.twitter.com/n5D9asYxex</a></p>— DEJAN (@dejanseo) <a href="https://twitter.com/dejanseo/status/1768497786205650948?ref_src=twsrc%5Etfw">March 15, 2024</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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