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--- |
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language: en |
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tags: |
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- exbert |
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license: mit |
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--- |
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# ColD Fusion BERT uncased model |
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Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets. |
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Full details at [this paper](https://arxiv.org/abs/2212.01378). |
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## Paper Abstract: |
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Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a |
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mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, |
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massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources |
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that are only available to well-resourced teams. |
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In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed |
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computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic |
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loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that |
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ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on |
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all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find |
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ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, |
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ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. |
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### How to use |
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Best way to use is to finetune on your own task, but you can also extract features directly. |
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To get the features of a given text in PyTorch: |
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```python |
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from transformers import RobertaTokenizer, RobertaModel |
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tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') |
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model = RobertaModel.from_pretrained('ibm/ColD-Fusion') |
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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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output = model(**encoded_input) |
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``` |
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and in TensorFlow: |
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```python |
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from transformers import RobertaTokenizer, TFRobertaModel |
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tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') |
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model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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## Evaluation results |
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See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) |
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When fine-tuned on downstream tasks, this model achieves the following results: |
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### BibTeX entry and citation info |
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```bibtex |
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@article{ColDFusion, |
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author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, |
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title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, |
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journal = {CoRR}, |
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volume = {abs/2212.01378}, |
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year = {2022}, |
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url = {https://arxiv.org/abs/2212.01378}, |
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archivePrefix = {arXiv}, |
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eprint = {2212.01378}, |
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} |
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``` |
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<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> |
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
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</a> |
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