FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally…
Open-weight model · Text classification
BioLinkBERT-base
by Michihiro Yasunaga michiyasunaga/BioLinkBERT-base
BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
Parameters—
Context512
Weights433.0 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads19.5k
Model Card
By Michihiro Yasunaga, published under apache-2.0, revision b71f5d70f063.
BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. 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…
Read Michihiro Yasunaga's full model card
BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository.
This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE.
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:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base')
model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-base')
inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
For fine-tuning, you can use this repository or follow any other BERT fine-tuning codebases.
Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
Biomedical benchmarks (BLURB, MedQA, MMLU, etc.): BioLinkBERT attains new state-of-the-art.
| BLURB score | PubMedQA | BioASQ | MedQA-USMLE | |
|---|---|---|---|---|
| PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 |
| BioLinkBERT-base | 83.39 | 70.2 | 91.4 | 40.0 |
| BioLinkBERT-large | 84.30 | 72.2 | 94.8 | 44.6 |
| MMLU-professional medicine | |
|---|---|
| GPT-3 (175 params) | 38.7 |
| UnifiedQA (11B params) | 43.2 |
| BioLinkBERT-large (340M params) | 50.7 |
Citation
If you find LinkBERT useful in your project, please cite the following:
@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)},
}
Configuration
- Architecture
- BertModel
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 28,895
- Model type
- bert
Identity and Version
- Repository
- michiyasunaga/BioLinkBERT-base
- Publisher
- Michihiro Yasunaga
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- b71f5d70f063d1c8f1124070ce86f1ee463ca1fe
- First published
- 2022-03-08
- Last updated
- 2022-03-31
Files and Weights
8 files, 433.7 MB in total. The weights are 1 file totalling 433.0 MB in bin.
Weights1 file · 433.0 MB
Configuration2 files · 671 B
Tokenizer3 files · 672.0 KB
Documentation1 file · 3.8 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 433.0 MB | acc5ae5f1620 |
| config.json | Configuration | 559 B | — |
| special_tokens_map.json | Configuration | 112 B | — |
| README.md | Documentation | 3.8 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 446.6 KB | — |
| tokenizer_config.json | Tokenizer | 379 B | — |
| vocab.txt | Tokenizer | 225.1 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 433.0 MB
Download from Michihiro Yasunaga
Released by Michihiro Yasunaga through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2203.15827
- Trained on (disclosed) pubmed
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 433.0 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About BioLinkBERT-base
Can I use BioLinkBERT-base commercially?
Yes. BioLinkBERT-base is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is BioLinkBERT-base's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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