Text Classification

Initialize a HappyTextClassification() object to perform text classification.

This model assigns a label to a given text string. For example, you can train a model to detect if an email is spam based on its text.

Initialization Arguments:

  1. model_type (string): specify the model name in all caps, such as “ROBERTA” or “ALBERT”
  2. model_name(string): below is a URL that contains potential models. The default is “distilbert-base-uncased” MODELS
  3. num_labels(int): The number of text categories. The default is 2
  4. use_auth_token (string): Specify the authentication token to load private models.
  5. trust_remote_code (bool): Allow for custom Python files to be used from the model_name location.

WARNING: If you try to load a pretrained model that has a different number of categories than num_labels, then you will get an error

NOTE: “albert-base-v2”, “bert-base-uncased” and “distilbert-base-uncased” do not have a predefined number of labels, so if you use these models you can set num_labels freely

Example 2.0:

from happytransformer import HappyTextClassification
# --------------------------------------#
happy_tc_distilbert = HappyTextClassification("DISTILBERT", "distilbert-base-uncased", num_labels=2)  # default 
happy_tc_albert = HappyTextClassification(model_type="ALBERT", model_name="albert-base-v2")
happy_tc_bert = HappyTextClassification("BERT", "bert-base-uncased")
happy_tc_roberta = HappyTextClassification("ROBERTA", "roberta-base")
happy_tc_private_roberta = HappyTextClassification("ROBERTA", "user-repo/roberta-base", use_auth_token="123abc")


Text classification (training)

Text classification (hate speech detection)

Text classification (sentiment analysis)

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