> For the complete documentation index, see [llms.txt](https://michael-mao.gitbook.io/sentiment-analysis-bert/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://michael-mao.gitbook.io/sentiment-analysis-bert/pytorch-bert/setup-the-bert-pretrained-model.md).

# Setup the BERT Pretrained Model

BERT pre-trained model is a good starting point for us to do a lot of NLP task, and we can use its performance as Benchmark. In this session, we simply load the model

## Load Package

the package is in the [`transformers` ](https://huggingface.co/transformers/index.html)library

```python
from transformers import BertForSequenceClassification
```

In here, we use the one for ”**Sequence Classification**” because the twitter data is a sequential data: a paragraph of text

## Loading the model

```python
Model = BertForSequenceClassification.from_pretrained(
    'bert-base-uncased', # the specific model we want to use, it is matching with the tokenizer we choose
    num_labels=len(label_dict), # how many classes / labels we need
    output_attentions=False, # whether the model tells us its reasoning in making that prediction
    output_hidden_states=False
)
```