HuggingFaceModel#
- class composer.models.HuggingFaceModel(model, tokenizer=None, use_logits=False, metrics=None)[source]#
A wrapper class that converts ๐ค Transformers models to composer models.
- Parameters
model (PreTrainedModel) โ A ๐ค Transformers model.
tokenizer (PreTrainedTokenizer) โ Tokenizer used to prepare the dataset and validate model inputs during training. Default
None
.use_logits (bool, optional) โ If True, the modelโs output logits will be used to calculate validation metrics. Else, metrics will be inferred from the HuggingFaceModel directly. Default:
False
metrics (list[Metric], optional) โ list of torchmetrics to apply to the output of validate. Default:
None
.
Warning
This wrapper is designed to work with ๐ค datasets that define a labels column.
Example:
import transformers from composer.models import HuggingFaceModel hf_model = transformers.AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) model = HuggingFaceModel(hf_model)
- get_model_inputs()[source]#
Returns a set of inputs that the model expects in the forward pass. If an algorithm wants to interact with the model inputs (for instance, popping the labels for a custom loss fn, or adding attention head masks for head pruning, it must access self.set_model_inputs(). :Returns: model_inputs โ The set of keys that are expected in the Mapping used to compute the forward pass.