readme: ---
  language: en
  tags:
  - exbert
  license: mit
  ---
  
  # ColD Fusion model
  
  Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. 
  Full details at [this paper](https://arxiv.org/abs/2212.01378).
  
  ## Paper Abstract:
  
  Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a 
  mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, 
  massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources 
  that are only available to well-resourced teams.
  
  In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed 
  computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic 
  loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that 
  ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on 
  all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find 
  ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, 
  ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
  
  
  ### How to use
  Best way to use is to finetune on your own task, but you can also extract features directly.
  To get the features of a given text in PyTorch:
  
  ```python
  from transformers import RobertaTokenizer, RobertaModel
  tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
  model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  and in TensorFlow:
  
  ```python
  from transformers import RobertaTokenizer, TFRobertaModel
  tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
  model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  ## Evaluation results
  
  ## Model Recycling
  
  [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.25&mnli_lp=nan&20_newsgroup=0.54&ag_news=0.03&amazon_reviews_multi=-0.32&anli=1.59&boolq=2.68&cb=19.73&cola=-0.22&copa=23.30&dbpedia=1.34&esnli=0.15&financial_phrasebank=2.99&imdb=-0.04&isear=1.06&mnli=0.31&mrpc=-0.86&multirc=2.50&poem_sentiment=1.63&qnli=-0.00&qqp=0.40&rotten_tomatoes=3.41&rte=12.80&sst2=1.30&sst_5bins=-0.30&stsb=1.38&trec_coarse=-0.11&trec_fine=2.64&tweet_ev_emoji=0.00&tweet_ev_emotion=1.22&tweet_ev_hate=1.55&tweet_ev_irony=6.37&tweet_ev_offensive=1.38&tweet_ev_sentiment=-0.60&wic=3.17&wnli=-6.90&wsc=-2.69&yahoo_answers=-0.53&model_name=ibm%2FColD-Fusion&base_name=roberta-base) using ibm/ColD-Fusion as a base model yields average score of 78.47 in comparison to 76.22 by roberta-base.
  
  The model is ranked 1st among all tested models for the roberta-base architecture as of 21/12/2022
  Results:
  
  |   20_newsgroup |   ag_news |   amazon_reviews_multi |    anli |   boolq |   cb |    cola |   copa |   dbpedia |   esnli |   financial_phrasebank |   imdb |   isear |    mnli |    mrpc |   multirc |   poem_sentiment |    qnli |    qqp |   rotten_tomatoes |     rte |    sst2 |   sst_5bins |    stsb |   trec_coarse |   trec_fine |   tweet_ev_emoji |   tweet_ev_emotion |   tweet_ev_hate |   tweet_ev_irony |   tweet_ev_offensive |   tweet_ev_sentiment |    wic |    wnli |     wsc |   yahoo_answers |
  |---------------:|----------:|-----------------------:|--------:|--------:|-----:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|-------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|-------:|--------:|--------:|----------------:|
  |        85.8205 |      89.8 |                  66.26 | 51.9375 | 81.3761 | 87.5 | 83.3174 |     72 |   78.6333 | 91.1441 |                   88.1 | 93.864 | 73.5332 | 87.2966 | 87.0098 |    63.717 |          85.5769 | 92.4034 | 91.113 |           91.8386 | 85.1986 | 95.4128 |     56.3801 | 91.2964 |            97 |        90.4 |           46.306 |            83.0401 |         54.4444 |          77.9337 |              85.9302 |              70.4331 | 68.652 | 47.8873 | 60.5769 |         71.8667 |
  
  
  For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
  See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycl