readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: deberta-v3-large
    results: []
  ---
  
  # deberta-v3-large-sentiment
  
  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.
  
  ## Model description
  
  Test set results:
  
  | Model                      | Emotion       | Hate          | Irony         | Offensive     | Sentiment     | 
  | -------------              | ------------- | ------------- | ------------- | ------------- | ------------- | 
  | deberta-v3-large           | **86.3**      | **61.3**      | **87.1**      | **86.4**      | **73.9**      | 
  | BERTweet                   | 79.3          | -             | 82.1          | 79.5          | 73.4          | 
  | RoB-RT                     | 79.5          | 52.3          | 61.7          | 80.5          | 69.3          | 
  
  [source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
  
  
  ## Intended uses & limitations
  
  Classifying attributes of interest on tweeter like data. 
  
  ## Training and evaluation data
  
  [tweet_eval](https://huggingface.co/datasets/tweet_eval)  dataset.
  
  ## Training procedure
  
  Fine tuned and evaluated with [run_glue.py]()
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7e-06
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 50
  - num_epochs: 10.0
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.2787        | 0.49  | 100  | 1.1127          | 0.4866   |
  | 1.089         | 0.98  | 200  | 0.9668          | 0.7139   |
  | 0.9134        | 1.47  | 300  | 0.8720          | 0.7834   |
  | 0.8618        | 1.96  | 400  | 0.7726          | 0.7941   |
  | 0.686         | 2.45  | 500  | 0.7337          | 0.8209   |
  | 0.6333        | 2.94  | 600  | 0.7350          | 0.8235   |
  | 0.5765        | 3.43  | 700  | 0.7561          | 0.8235   |
  | 0.5502        | 3.92  | 800  | 0.7273          | 0.8476   |
  | 0.5049        | 4.41  | 900  | 0.8137          | 0.8102   |
  | 0.4695        | 4.9   | 1000 | 0.7581          | 0.8289   |
  | 0.4657        | 5.39  | 1100 | 0.8404          | 0.8048   |
  | 0.4549        | 5.88  | 1200 | 0.7800          | 0.8369   |
  | 0.4305        | 6.37  | 1300 | 0.8575          | 0.8235   |
  | 0.4209        | 6.86  | 1400 | 0.8572          | 0.8102   |
  | 0.3983        | 7.35  | 1500 | 0.8392          | 0.8316   |
  | 0.4139        | 7.84  | 1600 | 0.8152          | 0.8209   |
  | 0.393         | 8.33  | 1700 | 0.8261          | 0.8289   |
  | 0.3979        | 8.82  | 1800 | 0.8328          | 0.8235   |
  | 0.3928        | 9.31  | 1900 | 0.8364          | 0.8209   |
  | 0.3848        | 9.8   | 2000 | 0.8322          | 0.8235   |
  
  
  ### Framework versions
  
  - Transformers 4.20.0.dev0
  - Pytorch 1.9.0
  - Datasets 2.2.2
  - Tokenizers 0.11.6
  