TOD-XLMR

TOD-XLMR is a conversationally specialized multilingual version based on XLM-RoBERTa. It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue by Wu et al. and first released in this repository.

The model is jointly trained with two objectives as proposed in TOD-BERT, including masked language modeling (MLM) and response contrastive loss (RCL). Masked language modeling is a common pretraining strategy utilized for BERT-based architectures, where a random sample of tokens in the input sequence is replaced with the special token [MASK] for predicting the original masked tokens. To further encourage the model to capture dialogic structure (i.e., dialog sequential order), response contrastive loss is implemented by using in-batch negative training with contrastive learning.

How to use

Here is how to use this model to get the features of a given text in PyTorch:

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR")
model = AutoModelForMaskedLM.from_pretrained("umanlp/TOD-XLMR")

# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)

Or you can also use AutoModel to load the pretrained model and further apply to downstream tasks:

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR")
model = AutoModel("umanlp/TOD-XLMR")

# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)