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metrics:
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- accuracy
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model-index:
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- name: deberta-v3-large
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results: []
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should probably proofread and complete it, then remove this comment. -->
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval/sentiment](https://huggingface.co/microsoft/deberta-v3-large) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3253
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- Accuracy: 0.7365
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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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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metrics:
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- accuracy
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model-index:
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- name: deberta-v3-large
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results: []
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# deberta-v3-large-sentiment
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
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## Model description
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Test set results:
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| Model | Emotion | Hate | Irony | Offensive | Sentiment |
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| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
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| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
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| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
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| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
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[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
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## Intended uses & limitations
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Classifying attributes of interest on tweeter like data.
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## Training and evaluation data
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[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
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## Training procedure
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Fine tuned and evaluated with [run_glue.py]()
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### Training hyperparameters
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The following hyperparameters were used during training:
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