chunked/nltk_chunking/tasks_sequence_classification/chunk_51.txt

Instantiate a pipeline for sentiment analysis with your model, and pass your text to it:

from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="stevhliu/my_awesome_model")
classifier(text)
[{'label': 'POSITIVE', 'score': 0.9994940757751465}]

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model")
inputs = tokenizer(text, return_tensors="pt")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model")
with torch.no_grad():
     logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
'POSITIVE'

Tokenize the text and return TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model")
inputs = tokenizer(text, return_tensors="tf")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model")
logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
model.config.id2label[predicted_class_id]
'POSITIVE'