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---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta_emo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_emo
This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
## Model Recycling
[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.24&mnli_lp=nan&20_newsgroup=0.54&ag_news=0.46&amazon_reviews_multi=-0.50&anli=1.81&boolq=2.93&cb=21.52&cola=-0.12&copa=22.30&dbpedia=0.20&esnli=-0.30&financial_phrasebank=0.99&imdb=-0.12&isear=0.54&mnli=-0.16&mrpc=0.37&multirc=2.85&poem_sentiment=4.52&qnli=0.47&qqp=0.24&rotten_tomatoes=2.95&rte=10.99&sst2=1.64&sst_5bins=0.79&stsb=1.59&trec_coarse=0.09&trec_fine=3.44&tweet_ev_emoji=-0.31&tweet_ev_emotion=0.65&tweet_ev_hate=-0.40&tweet_ev_irony=4.08&tweet_ev_offensive=2.08&tweet_ev_sentiment=-0.16&wic=3.02&wnli=-8.31&wsc=0.19&yahoo_answers=-0.14&model_name=gustavecortal%2Froberta_emo&base_name=roberta-base) using gustavecortal/roberta_emo as a base model yields average score of 78.47 in comparison to 76.22 by roberta-base.
The model is ranked 2nd among all tested models for the roberta-base architecture as of 18/01/2023
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 | 90.2333 | 66.08 | 52.1563 | 81.6208 | 89.2857 | 83.4132 | 71 | 77.5 | 90.6963 | 86.1 | 93.776 | 73.0117 | 86.8186 | 88.2353 | 64.0677 | 88.4615 | 92.8794 | 90.9523 | 91.3696 | 83.3935 | 95.7569 | 57.4661 | 91.5106 | 97.2 | 91.2 | 45.994 | 82.4771 | 52.4916 | 75.6378 | 86.6279 | 70.8727 | 68.4953 | 46.4789 | 63.4615 | 72.2667 |
For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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