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
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6362        | 0.18  | 100  | 0.5481          | 0.7197   |
  | 0.4264        | 0.36  | 200  | 0.4550          | 0.8008   |
  | 0.4174        | 0.53  | 300  | 0.4524          | 0.7868   |
  | 0.4197        | 0.71  | 400  | 0.4586          | 0.7918   |
  | 0.3819        | 0.89  | 500  | 0.4368          | 0.8078   |
  | 0.3558        | 1.07  | 600  | 0.4525          | 0.8068   |
  | 0.2982        | 1.24  | 700  | 0.4999          | 0.7928   |
  | 0.2885        | 1.42  | 800  | 0.5129          | 0.8108   |
  | 0.253         | 1.6   | 900  | 0.5873          | 0.8208   |
  | 0.3354        | 1.78  | 1000 | 0.4244          | 0.8178   |
  | 0.3083        | 1.95  | 1100 | 0.4853          | 0.8058   |
  | 0.2301        | 2.13  | 1200 | 0.7209          | 0.8018   |
  | 0.2167        | 2.31  | 1300 | 0.8090          | 0.7778   |
  | 0.1863        | 2.49  | 1400 | 0.6812          | 0.8038   |
  | 0.2181        | 2.66  | 1500 | 0.6958          | 0.8138   |
  | 0.2159        | 2.84  | 1600 | 0.6315          | 0.8118   |
  | 0.1828        | 3.02  | 1700 | 0.7173          | 0.8138   |
  | 0.1287        | 3.2   | 1800 | 0.9081          | 0.8018   |
  | 0.1711        | 3.37  | 1900 | 0.8858          | 0.8068   |
  | 0.1598        | 3.55  | 2000 | 0.7878          | 0.8028   |
  | 0.1467        | 3.73  | 2100 | 0.9003          | 0.7948   |
  | 0.127         | 3.91  | 2200 | 0.9066          | 0.8048   |
  | 0.1134        | 4.09  | 2300 | 0.9646          | 0.8118   |
  | 0.1017        | 4.26  | 2400 | 0.9778          | 0.8048   |
  | 0.085         | 4.44  | 2500 | 1.0529          | 0.8088   |
  | 0.0996        | 4.62  | 2600 | 1.0082          | 0.8058   |
  | 0.1054        | 4.8   | 2700 | 0.9698          | 0.8108   |
  | 0.1375        | 4.97  | 2800 | 0.9334          | 0.8048   |
  | 0.0487        | 5.15  | 2900 | 1.1273          | 0.8108   |
  | 0.0611        | 5.33  | 3000 | 1.1528          | 0.8058   |
  | 0.0668        | 5.51  | 3100 | 1.0148          | 0.8118   |
  | 0.0582        | 5.68  | 3200 | 1.1333          | 0.8108   |
  | 0.0869        | 5.86  | 3300 | 1.0607          | 0.8088   |
  | 0.0623        | 6.04  | 3400 | 1.1880          | 0.8068   |
  | 0.0317        | 6.22  | 3500 | 1.2836          | 0.8008   |
  | 0.0546        | 6.39  | 3600 | 1.2148          | 0.8058   |
  | 0.0486        | 6.57  | 3700 | 1.3348          | 0.8008   |
  | 0.0332        | 6.75  | 3800 | 1.3734          | 0.8018   |
  | 0.051         | 6.93  | 3900 | 1.2966          | 0.7978   |
  | 0.0217        | 7.1   | 4000 | 1.3853          | 0.8048   |
  | 0.0109        | 7.28  | 4100 | 1.4803          | 0.8068   |
  | 0.0345        | 7.46  | 4200 | 1.4906          | 0.7998   |
  | 0.0365        | 7.64  | 4300 | 1.4347          | 0.8028   |
  | 0.0265        | 7.82  | 4400 | 1.3977          | 0.8128   |
  | 0.0257        | 7.99  | 4500 | 1.3705          | 0.8108   |
  | 0.0036        | 8.17  | 4600 | 1.4353          | 0.8168   |
  | 0.0269        | 8.35  | 4700 | 1.4826          | 0.8068   |
  | 0.0231        | 8.53  | 4800 | 1.4811          | 0.8118   |
  | 0.0204        | 8.7   | 4900 | 1.5245          | 0.8028   |
  | 0.0263        | 8.88  | 5000 | 1.5123          | 0.8018   |
  | 0.0138        | 9.06  | 5100 | 1.5113          | 0.8028   |
  | 0.0089        | 9.24  | 5200 | 1.5846          | 0.7978   |
  | 0.029         | 9.41