Create README.md

README.md

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+---
+tags:
+- exbert
+language: multilingual
+license: mit
+---
+
+# TOD-XLMR
+
+TOD-XLMR is a conversationally specialized version of [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)
+```