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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-base__sst2__all-train |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-base__sst2__all-train |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6964 |
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- Accuracy: 0.49 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 7 | 0.6964 | 0.49 | |
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| No log | 2.0 | 14 | 0.7010 | 0.49 | |
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| No log | 3.0 | 21 | 0.7031 | 0.49 | |
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| No log | 4.0 | 28 | 0.7054 | 0.49 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2 |
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- Tokenizers 0.10.3 |
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## Model Recycling |
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.10&mnli_lp=nan&20_newsgroup=0.06&ag_news=0.36&amazon_reviews_multi=0.08&anli=0.63&boolq=1.45&cb=3.57&cola=0.39&copa=-1.40&dbpedia=0.57&esnli=-0.53&financial_phrasebank=1.52&imdb=-0.04&isear=-0.22&mnli=-0.19&mrpc=0.99&multirc=2.00&poem_sentiment=0.77&qnli=-0.19&qqp=0.21&rotten_tomatoes=-0.18&rte=-0.76&sst2=-0.34&sst_5bins=-0.60&stsb=-0.32&trec_coarse=0.24&trec_fine=-0.22&tweet_ev_emoji=0.82&tweet_ev_emotion=0.50&tweet_ev_hate=-3.92&tweet_ev_irony=-0.99&tweet_ev_offensive=-0.17&tweet_ev_sentiment=-0.96&wic=1.20&wnli=-2.61&wsc=2.26&yahoo_answers=-0.27&model_name=SetFit%2Fdeberta-v3-base__sst2__all-train&base_name=microsoft%2Fdeberta-v3-base) using SetFit/deberta-v3-base__sst2__all-train as a base model yields average score of 79.14 in comparison to 79.04 by microsoft/deberta-v3-base. |
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The model is ranked 3rd among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023 |
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Results: |
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| 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 | |
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|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|-------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:| |
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| 86.4711 | 90.8 | 66.94 | 59.4063 | 84.4343 | 78.5714 | 86.9607 | 57 | 80 | 91.3986 | 86 | 94.452 | 71.6428 | 89.5952 | 90.1961 | 64.2533 | 87.5 | 93.3187 | 91.9936 | 90.2439 | 81.5884 | 94.7248 | 56.3801 | 89.96 | 98 | 90.8 | 47.014 | 84.4476 | 52.2896 | 78.8265 | 84.8837 | 70.8401 | 72.4138 | 67.6056 | 66.3462 | 71.7667 | |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/) |
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