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--- |
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language: "en" |
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tags: |
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- chemical-domain |
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- safety-datasheets |
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widget: |
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- text: "The removal of mercaptans, and for drying of gases and [MASK]." |
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--- |
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# BERT for Chemical Industry |
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A BERT-based language model further pre-trained from the checkpoint of [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased). We used a corpus of over 40,000+ technical documents from the **Chemical Industrial domain** and combined it with 13,000 Wikipedia Chemistry articles, ranging from Safety Data Sheets and Products Information Documents, with 250,000+ tokens from the Chemical domain and pre-trained using MLM and over 9.2 million paragraphs. |
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- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run |
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the entire masked sentence through the model and has to predict the masked words. This is different from traditional |
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recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like |
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GPT internally masks the future tokens. It allows the model to learn a bidirectional representation of the |
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sentence. |
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```python |
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from transformers import pipeline |
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fill_mask = pipeline( |
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"fill-mask", |
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model="recobo/chemical-bert-uncased", |
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tokenizer="recobo/chemical-bert-uncased" |
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) |
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fill_mask("we create [MASK]") |
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``` |
