Hi @dikster99, I had a closer look at the multi-label example I linked to and see that it’s more complicated than it needs to be because:


transformers now has a Trainer class that dramatically simplifies the training / evaluation loops.
the datasets library is a much better way to prepare the data and works great with the Trainer


To implement multi-label classification, the main thing you need to do is override the forward method of BertForSequenceClassification to compute the loss with a sigmoid instead of softmax applied to the logits. In PyTorch it looks something like
class BertForMultilabelSequenceClassification(BertForSequenceClassification):
    def __init__(self, config):
      super().__init__(config)

    def forward(self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bert(input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict)

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            loss_fct = torch.nn.BCEWithLogitsLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), 
                            labels.float().view(-1, self.num_labels))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions)

where the only thing that I’ve really changed are these two lines
loss_fct = torch.nn.BCEWithLogitsLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.float().view(-1, self.num_labels))

You can probably adapt the TensorFlow code in a similar fashion (I haven’t used TF in years so can’t be much help there ).
There are some other things needed (e.g. the metrics), so I put together a hacky notebook here that you can use as a template to get started: https://colab.research.google.com/drive/1X7l8pM6t4VLqxQVJ23ssIxmrsc4Kpc5q?usp=sharing 154