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--- |
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language: en |
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tags: |
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- exbert |
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license: apache-2.0 |
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datasets: |
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- Confidential |
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--- |
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# BERT base model (uncased) |
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in |
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[this paper](https://arxiv.org/abs/1810.04805) and first released in |
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference |
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between english and English. |
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## Model description |
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BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it |
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was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of |
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publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it |
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was pretrained with two objectives: |
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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 which internally mask the future tokens. It allows the model to learn a bidirectional representation of the |
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sentence. |
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- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes |
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they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to |
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predict if the two sentences were following each other or not. |
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This way, the model learns an inner representation of the English language that can then be used to extract features |
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard |
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classifier using the features produced by the BERT model as inputs. |
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## Model description [sbcBI/sentiment_analysis] |
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This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for |
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further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification. |
