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---
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license: apache-2.0
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language: en
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datasets:
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- wikipedia
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- bookcorpus
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tags:
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- bert
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- exbert
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- linkbert
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- feature-extraction
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- fill-mask
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- question-answering
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- text-classification
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- token-classification
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---
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## LinkBERT-base
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LinkBERT-base 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).
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## Model description
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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.
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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).
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## Intended uses & limitations
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The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
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You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
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### How to use
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To use the model to get the features of a given text in PyTorch:
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```python
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/LinkBERT-base')
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model = AutoModel.from_pretrained('michiyasunaga/LinkBERT-base')
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inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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outputs = model(**inputs)
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last_hidden_states = outputs.last_hidden_state
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```
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For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
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## Evaluation results
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When fine-tuned on downstream tasks, LinkBERT achieves the following results.
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**General benchmarks ([MRQA](https://github.com/mrqa/MRQA-Shared-Task-2019) and [GLUE](https://gluebenchmark.com/)):**
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| | HotpotQA | TriviaQA | SearchQA | NaturalQ | NewsQA | SQuAD | GLUE |
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| ---------------------- | -------- | -------- | -------- | -------- | ------ | ----- | -------- |
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| | F1 | F1 | F1 | F1 | F1 | F1 | Avg score |
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| BERT-base | 76.0 | 70.3 | 74.2 | 76.5 | 65.7 | 88.7 | 79.2 |
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| **LinkBERT-base** | **78.2** | **73.9** | **76.8** | **78.3** | **69.3** | **90.1** | **79.6** |
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| BERT-large | 78.1 | 73.7 | 78.3 | 79.0 | 70.9 | 91.1 | 80.7 |
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| **LinkBERT-large** | **80.8** | **78.2** | **80.5** | **81.0** | **72.6** | **92.7** | **81.1** |
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## Citation
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If you find LinkBERT useful in your project, please cite the following:
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```bibtex
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@InProceedings{yasunaga2022linkbert,
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author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
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title = {LinkBERT: Pretraining Language Models with Document Links},
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year = {2022},
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booktitle = {Association for Computational Linguistics (ACL)},
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}
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```
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