readme: ---
  tags:
  - exbert
  language: multilingual
  license: mit
  ---
  
  # TOD-XLMR
  
  TOD-XLMR is a conversationally specialized multilingual version based on [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base). 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](https://aclanthology.org/2020.emnlp-main.66.pdf) by Wu et al. and first released in [this repository](https://huggingface.co/TODBERT). 
  
  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)
  ```
  