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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' |