leverages a fine-tuned model on sst2, which is a GLUE task.
This returns a label ("POSITIVE" or "NEGATIVE") alongside a score, as follows:
```py
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("I hate you")[0]
print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
label: NEGATIVE, with score: 0.9991
result = classifier("I love you")[0]
print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
label: POSITIVE, with score: 0.9999
```
Here is an example of doing a sequence classification using a model to determine if two sequences are paraphrases of
each other. The process is the following:
Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it
   with the weights stored in the checkpoint.
