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--- |
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language: en |
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tags: |
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- transformers |
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- feature-extraction |
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- materials |
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license: other |
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--- |
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# MaterialsBERT |
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This model is a fine-tuned version of [PubMedBERT model](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on a dataset of 2.4 million materials science abstracts. |
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It was introduced in [this](https://www.nature.com/articles/s41524-023-01003-w) paper. This model is uncased. |
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## Model description |
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Domain-specific fine-tuning has been [shown](https://arxiv.org/abs/2007.15779) to improve performance in downstream performance on a variety of NLP tasks. MaterialsBERT fine-tunes PubMedBERT, a pre-trained language model trained using biomedical literature. This model was chosen as the biomedical domain is close to the materials science domain. MaterialsBERT when further fine-tuned on a variety of downstream sequence labeling tasks in materials science, outperformed other baseline language models tested on three out of five datasets. |
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## Intended uses & limitations |
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to |
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be fine-tuned on materials-science relevant downstream tasks. |
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Note that this model is primarily aimed at being fine-tuned on tasks that use a sentence or a paragraph (potentially masked) |
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to make decisions, such as sequence classification, token classification or question answering. |
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## How to Use |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import BertForMaskedLM, BertTokenizer |
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tokenizer = BertTokenizer.from_pretrained('pranav-s/MaterialsBERT') |
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model = BertForMaskedLM.from_pretrained('pranav-s/MaterialsBERT') |
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text = "Enter any text you like" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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## Training data |
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A fine-tuning corpus of 2.4 million materials science abstracts was used. The DOI's of the journal articles used are provided in the file training_DOI.txt |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.2 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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## Citation |
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If you find MaterialsBERT useful in your research, please cite the following paper: |
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```latex |
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@article{materialsbert, |
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title={A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing}, |
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author={Shetty, Pranav and Rajan, Arunkumar Chitteth and Kuenneth, Chris and Gupta, Sonakshi and Panchumarti, Lakshmi Prerana and Holm, Lauren and Zhang, Chao and Ramprasad, Rampi}, |
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journal={npj Computational Materials}, |
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volume={9}, |
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number={1}, |
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pages={52}, |
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year={2023}, |
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publisher={Nature Publishing Group UK London} |
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} |
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``` |
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<a href="https://huggingface.co/exbert/?model=pranav-s/MaterialsBERT"> |
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
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</a> |
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