added FlaubertForSequenceClassification

simpletransformers/classification/classification_model.py

@@ -48,6 +48,8 @@
     CamembertTokenizer,
     XLMRobertaConfig,
     XLMRobertaTokenizer,
+    FlaubertConfig,
+    FlaubertTokenizer,
 )
 
 from simpletransformers.classification.classification_utils import (
@@ -63,6 +65,7 @@
 from simpletransformers.classification.transformer_models.albert_model import AlbertForSequenceClassification
 from simpletransformers.classification.transformer_models.camembert_model import CamembertForSequenceClassification
 from simpletransformers.classification.transformer_models.xlm_roberta_model import XLMRobertaForSequenceClassification
+from simpletransformers.classification.transformer_models.flaubert_model import FlaubertForSequenceClassification
 
 from simpletransformers.config.global_args import global_args
 
@@ -97,6 +100,7 @@ def __init__(
             "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
             "camembert": (CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer),
             "xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
+            "flaubert": (FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer),
         }
 
         if args and 'manual_seed' in args:

simpletransformers/classification/transformer_models/flaubert_model.py

@@ -0,0 +1,85 @@
+from transformers.modeling_xlm import SequenceSummary, XLMModel, XLMPreTrainedModel
+from transformers.modeling_flaubert import FlaubertModel
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss, MSELoss
+
+
+class FlaubertForSequenceClassification(XLMPreTrainedModel):
+    r"""
+        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
+            Labels for computing the sequence classification/regression loss.
+            Indices should be in ``[0, ..., config.num_labels - 1]``.
+            If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
+            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
+    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
+        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
+            Classification (or regression if config.num_labels==1) loss.
+        **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
+            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
+            of shape ``(batch_size, sequence_length, hidden_size)``:
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+    Examples::
+        tokenizer = FlaubertTokenizer.from_pretrained('flaubert-base-uncased')
+        model = FlaubertForSequenceClassification.from_pretrained('flaubert-base-uncased')
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+        outputs = model(input_ids, labels=labels)
+        loss, logits = outputs[:2]
+    """  # noqa: ignore flake8"
+
+    def __init__(self, config, weight=None):
+        super(FlaubertForSequenceClassification, self).__init__(config)
+        self.num_labels = config.num_labels
+        self.weight = weight
+
+        self.transformer = FlaubertModel(config)
+        self.sequence_summary = SequenceSummary(config)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        langs=None,
+        token_type_ids=None,
+        position_ids=None,
+        lengths=None,
+        cache=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+    ):
+        transformer_outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            langs=langs,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            lengths=lengths,
+            cache=cache,
+            head_mask=head_mask,
+        )
+
+        output = transformer_outputs[0]
+        logits = self.sequence_summary(output)
+
+        outputs = (logits,) + transformer_outputs[1:]  # Keep new_mems and attention/hidden states if they are here
+
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss(weight=self.weight)
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            outputs = (loss,) + outputs
+
+        return outputs