readme: ---
  license: apache-2.0
  language: en
  datasets:
  - wikipedia
  - bookcorpus
  tags:
  - bert
  - exbert
  - linkbert
  - feature-extraction
  - fill-mask
  - question-answering
  - text-classification
  - token-classification
  ---
  
  ## LinkBERT-large
  
  LinkBERT-large model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
  
  
  ## Model description
  
  LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
  
  LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
  
  
  ## Intended uses & limitations
  
  The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
  You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
  
  
  ### How to use
  
  To use the model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/LinkBERT-large')
  model = AutoModel.from_pretrained('michiyasunaga/LinkBERT-large')
  inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
  outputs = model(**inputs)
  last_hidden_states = outputs.last_hidden_state
  ```
  
  For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
  
  
  ## Evaluation results
  
  When fine-tuned on downstream tasks, LinkBERT achieves the following results.
  
  **General benchmarks ([MRQA](https://github.com/mrqa/MRQA-Shared-Task-2019) and [GLUE](https://gluebenchmark.com/)):**
  
  |                         | HotpotQA | TriviaQA | SearchQA | NaturalQ | NewsQA   | SQuAD    | GLUE      |
  | ----------------------  | -------- | -------- | -------- | -------- | ------   | -----    | --------  |
  |                         | F1       | F1       | F1       |  F1      | F1       | F1       | Avg score |
  | BERT-base               | 76.0     | 70.3     | 74.2     | 76.5     | 65.7     | 88.7     | 79.2      |
  | **LinkBERT-base**       | **78.2** | **73.9** | **76.8** | **78.3** | **69.3** | **90.1** | **79.6**  |
  | BERT-large              | 78.1     | 73.7     | 78.3     | 79.0     | 70.9     | 91.1     | 80.7      |
  | **LinkBERT-large**      | **80.8** | **78.2** | **80.5** | **81.0** | **72.6** | **92.7** | **81.1**  |
  
  
  ## Citation
  
  If you find LinkBERT useful in your project, please cite the following:
  
  ```bibtex
  @InProceedings{yasunaga2022linkbert,
    author =  {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
    title =   {LinkBERT: Pretraining Language Models with Document Links},
    year =    {2022},  
    booktitle = {Association for Computational Linguistics (ACL)},  
  }
  ```
  