Using BERT for downstream tasks typically involves fine-tuning the pre-trained BERT model on a specific dataset related to your task. Below is a simplified example of how you can use the Transformers library to fine-tune BERT for text classification. This example assumes you have a dataset for sentiment analysis.

```python

import torch

from https://torch.utils.data/ import DataLoader, Dataset

from transformers import BertTokenizer, BertForSequenceClassification, AdamW

# Step 1: Load Pre-trained BERT Model and Tokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Step 2: Prepare your Dataset

class SentimentDataset(Dataset):

def __init__(self, texts, labels):

self.texts = texts

self.labels = labels

def __len__(self):

      return len(self.texts)

def __getitem__(self, idx):

      return {'text': self.texts[idx], 'label': self.labels[idx]}

# Example data

train_texts = ["This is a positive sentence.", "Negative sentiment here."]

train_labels = [1, 0] # 1 for positive, 0 for negative

train_dataset = SentimentDataset(train_texts, train_labels)

train_dataloader = DataLoader(train_dataset, batch_size=2, shuffle=True)

# Step 3: Fine-tune BERT on your Task

optimizer = AdamW(model.parameters(), lr=5e-5)

# Fine-tuning loop

for epoch in range(3): # Replace 3 with the desired number of epochs

for batch in train_dataloader:

inputs = tokenizer(batch['text'], return_tensors='pt', padding=True, truncation=True)

labels = torch.tensor(batch['label']).unsqueeze(0)

outputs = model(**inputs, labels=labels)

loss = outputs.loss

loss.backward()

optimizer.step()

optimizer.zero_grad()

# Step 4: Save the Fine-tuned Model

model.save_pretrained('fine_tuned_bert_sentiment')

# Step 5: Inference with the Fine-tuned Model

text_to_classify = "This is a test sentence."

inputs = tokenizer(text_to_classify, return_tensors='pt', padding=True, truncation=True)

outputs = model(**inputs)

# Get predicted class (0 or 1 for binary classification)

predicted_class = torch.argmax(outputs.logits).item()

print(f"Predicted class for '{text_to_classify}': {predicted_class}")

```

Note:

1. Ensure you replace the dataset and labels with your actual data.

2. Fine-tuning might require adjusting hyperparameters, such as learning rate, batch size, and the number of epochs.

3. This example is simplified and may need modifications based on your specific task and dataset.

4. Always refer to the Transformers library documentation for the most up-to-date information: https://huggingface.co/transformers/

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