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: 5e-06
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.0614        | 0.07  | 100   | 1.0196          | 0.4345   |
  | 0.8601        | 0.14  | 200   | 0.7561          | 0.6460   |
  | 0.734         | 0.21  | 300   | 0.6796          | 0.6955   |
  | 0.6753        | 0.28  | 400   | 0.6521          | 0.7000   |
  | 0.6408        | 0.35  | 500   | 0.6119          | 0.7440   |
  | 0.5991        | 0.42  | 600   | 0.6034          | 0.7370   |
  | 0.6069        | 0.49  | 700   | 0.5976          | 0.7375   |
  | 0.6122        | 0.56  | 800   | 0.5871          | 0.7425   |
  | 0.5908        | 0.63  | 900   | 0.5935          | 0.7445   |
  | 0.5884        | 0.7   | 1000  | 0.5792          | 0.7520   |
  | 0.5839        | 0.77  | 1100  | 0.5780          | 0.7555   |
  | 0.5772        | 0.84  | 1200  | 0.5727          | 0.7570   |
  | 0.5895        | 0.91  | 1300  | 0.5601          | 0.7550   |
  | 0.5757        | 0.98  | 1400  | 0.5613          | 0.7525   |
  | 0.5121        | 1.05  | 1500  | 0.5867          | 0.7600   |
  | 0.5254        | 1.12  | 1600  | 0.5595          | 0.7630   |
  | 0.5074        | 1.19  | 1700  | 0.5594          | 0.7585   |
  | 0.4947        | 1.26  | 1800  | 0.5697          | 0.7575   |
  | 0.5019        | 1.33  | 1900  | 0.5665          | 0.7580   |
  | 0.5005        | 1.4   | 2000  | 0.5484          | 0.7655   |
  | 0.5125        | 1.47  | 2100  | 0.5626          | 0.7605   |
  | 0.5241        | 1.54  | 2200  | 0.5561          | 0.7560   |
  | 0.5198        | 1.61  | 2300  | 0.5602          | 0.7600   |
  | 0.5124        | 1.68  | 2400  | 0.5654          | 0.7490   |
  | 0.5096        | 1.75  | 2500  | 0.5803          | 0.7515   |
  | 0.4885        | 1.82  | 2600  | 0.5889          | 0.75     |
  | 0.5111        | 1.89  | 2700  | 0.5508          | 0.7665   |
  | 0.4868        | 1.96  | 2800  | 0.5621          | 0.7635   |
  | 0.4599        | 2.04  | 2900  | 0.5995          | 0.7615   |
  | 0.4147        | 2.11  | 3000  | 0.6202          | 0.7530   |
  | 0.4233        | 2.18  | 3100  | 0.5875          | 0.7625   |
  | 0.4324        | 2.25  | 3200  | 0.5794          | 0.7610   |
  | 0.4141        | 2.32  | 3300  | 0.5902          | 0.7460   |
  | 0.4306        | 2.39  | 3400  | 0.6053          | 0.7545   |
  | 0.4266        | 2.46  | 3500  | 0.5979          | 0.7570   |
  | 0.4227        | 2.53  | 3600  | 0.5920          | 0.7650   |
  | 0.4226        | 2.6   | 3700  | 0.6166          | 0.7455   |
  | 0.3978        | 2.67  | 3800  | 0.6126          | 0.7560   |
  | 0.3954        | 2.74  | 3900  | 0.6152          | 0.7550   |
  | 0.4209        | 2.81  | 4000  | 0.5980          | 0.75     |
  | 0.3982        | 2.88  | 4100  | 0.6096          | 0.7490   |
  | 0.4016        | 2.95  | 4200  | 0.6541          | 0.7425   |
  | 0.3966        | 3.02  | 4300  | 0.6377          | 0.7545   |
  | 0.3074        | 3.09  | 4400  | 0.6860          | 0.75     |
  | 0.3551        | 3.16  | 4500  | 0.6160          | 0.7550   |
  | 0.3323        | 3.23  | 4600  | 0.6714          | 0.7520   |
  | 0.3171        | 3.3   | 4700  | 0.6538          | 0.7535   |
  | 0.3403        | 3.37  | 4800  | 0.6774          | 0.7465   |
  | 0.3396        | 3.44  | 4900  | 0.6726          | 0.7465   |
  | 0.3259        | 3.51  | 5000  | 0.6465          | 0.7480   |
  | 0.3392        | 3.58  | 5100 