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--- |
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tags: |
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- exbert |
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language: multilingual |
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license: mit |
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--- |
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# TOD-XLMR |
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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). |
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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. |
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### How to use |
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Here is how to use this model to get the features of a given text in PyTorch: |
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``` |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR") |
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model = AutoModelForMaskedLM.from_pretrained("umanlp/TOD-XLMR") |
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# prepare input |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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# forward pass |
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output = model(**encoded_input) |
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``` |
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Or you can also use `AutoModel` to load the pretrained model and further apply to downstream tasks: |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR") |
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model = AutoModel("umanlp/TOD-XLMR") |
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# prepare input |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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# forward pass |
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output = model(**encoded_input) |
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``` |
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