<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
What is the best way to import the BERT Language model into python (PyTorch)?<|im_end|>
<|im_start|>assistant
The easiest way to import the BERT language model into python for use with PyTorch is using the Hugging Face Transformer's library, which has built in methods for pre-training, inference, and deploying BERT.

```
from transformers import AutoTokenizer, BertModel
import torch

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
```<|im_end|>
<|im_start|>user
After following these steps, how can I use the loaded model to predict a masked word?<|im_end|>
<|im_start|>assistant
That's actually not possible with the base `BertModel` class, which only returns a sentence embedding. HuggingFace's recommended way way to use BERT for masked word prediction is through the `pipeline` module:

```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='bert-base-uncased')
print(unmasker("Revenge is a dish best served [MASK]."))
# [
#   {'score': 0.3299422264099121, 'token': 3147, 'token_str': 'cold', 'sequence': 'revenge is a dish best served cold.'},
#   {'score': 0.07907173037528992, 'token': 19350, 'token_str': 'outdoors', 'sequence': 'revenge is a dish best served outdoors.'},
#   ...
# ]
```<|im_end|>
<|im_start|>user
That didn't work, I get the following error:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'bert'
```

What should I do instead?<|im_end|>
<|im_start|>assistant
Try this instead:
import torch
from transformers import BertTokenizer, BertModel

# Load the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Encode a text input
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1

# Load the BERT model
model = BertModel.from_pretrained('bert-base-uncased')

# Put the model in evaluation mode
model.eval()

# Predict hidden states features for each layer
with torch.no_grad():
    outputs = model(input_ids)
    last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple<|im_end|>