metadata
language:
- ar
- fr
- es
- de
- el
- bg
- ru
- tr
- vi
- th
- zh
- hi
- sw
- ur
datasets:
- xnli
- Babelscape/REDFM
widget:
- text: >-
The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea,
Hillel Slovak and Jack Irons. [SEP] Jack Irons place of birth Los Angeles
Model Card for mdeberta-v3-base-triplet-critic-xnli
This is the Triplit Critic model presented in the ACL 2023 paper RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset. If you use the model, please reference this work in your paper:
@inproceedings{huguet-cabot-et-al-2023-redfm-dataset,
title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset",
author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and
Navigli, Roberto",
booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2306.09802",
}
The Triplit Critic is based on mdeberta-v3-base and it was trained as a multitask system to filter triplets (triplet filtering data used can be found in Triplet-Critic-Data) as well as on the XNLI dataset. The model weights contain the two classification heads, however loading it using the huggingface library will only load those for Triplet filtering (ie. a binary classification head), if one wants to use it for XNLI it needs a custom script. While it is defined and trained as a classification system, we use the positive score (ie. Label_1) as the confidence score for a triplet. For SREDFM the confidence score thresshold was set at 0.75.
To load the multitask model:
from transformers import DebertaV2PreTrainedModel, DebertaV2Model
from torch import nn
from transformers.models.deberta_v2.modeling_deberta_v2 import *
from transformers.file_utils import ModelOutput
@dataclass
class TXNLIClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
logits_xnli (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(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.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
logits_xnli: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class DebertaV2ForTripletClassification(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
num_labels = getattr(config, "num_labels", 2)
self.num_labels = num_labels
self.deberta = DebertaV2Model(config)
self.pooler = ContextPooler(config)
output_dim = self.pooler.output_dim
self.classifier = nn.Linear(output_dim, num_labels)
drop_out = getattr(config, "cls_dropout", None)
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
self.dropout = StableDropout(drop_out)
self.classifier_xnli = nn.Linear(output_dim, 3)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.deberta.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
self.deberta.set_input_embeddings(new_embeddings)
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
encoder_layer = outputs[0]
pooled_output = self.pooler(encoder_layer)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
logits_xnli = self.classifier_xnli(pooled_output)
loss = None
if labels is not None:
if labels.dtype != torch.bool:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
else:
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits_xnli.view(-1, 3), labels.view(-1).long())
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TXNLIClassifierOutput(
loss=loss, logits=logits, logits_xnli=logits_xnli, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
License
This model is licensed under the CC BY-SA 4.0 license. The text of the license can be found here.