readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: deberta-v3-large-ddlm
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # deberta-v3-large-ddlm
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/models/microsoft/deberta-v3-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5241
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 0.9823        | 0.01  | 1000   | 0.9163          |
  | 0.8817        | 0.02  | 2000   | 0.9022          |
  | 0.9647        | 0.03  | 3000   | 0.8879          |
  | 0.8646        | 0.04  | 4000   | 0.8577          |
  | 0.9159        | 0.06  | 5000   | 0.8677          |
  | 0.8449        | 0.07  | 6000   | 0.8221          |
  | 0.8681        | 0.08  | 7000   | 0.8332          |
  | 0.8738        | 0.09  | 8000   | 0.8334          |
  | 0.8638        | 0.1   | 9000   | 0.8236          |
  | 0.9066        | 0.11  | 10000  | 0.8200          |
  | 0.8686        | 0.12  | 11000  | 0.8092          |
  | 0.7736        | 0.13  | 12000  | 0.8199          |
  | 0.8054        | 0.14  | 13000  | 0.7972          |
  | 0.8934        | 0.16  | 14000  | 0.7998          |
  | 0.7884        | 0.17  | 15000  | 0.7895          |
  | 0.8278        | 0.18  | 16000  | 0.7586          |
  | 0.8482        | 0.19  | 17000  | 0.7562          |
  | 0.8716        | 0.2   | 18000  | 0.7819          |
  | 0.8881        | 0.21  | 19000  | 0.7878          |
  | 0.8397        | 0.22  | 20000  | 0.7989          |
  | 0.811         | 0.23  | 21000  | 0.7846          |
  | 0.7762        | 0.24  | 22000  | 0.7753          |
  | 0.7778        | 0.25  | 23000  | 0.7878          |
  | 0.737         | 0.27  | 24000  | 0.7473          |
  | 0.8451        | 0.28  | 25000  | 0.7460          |
  | 0.823         | 0.29  | 26000  | 0.7300          |
  | 0.7472        | 0.3   | 27000  | 0.7292          |
  | 0.8048        | 0.31  | 28000  | 0.7697          |
  | 0.7962        | 0.32  | 29000  | 0.7359          |
  | 0.8048        | 0.33  | 30000  | 0.7409          |
  | 0.8095        | 0.34  | 31000  | 0.7434          |
  | 0.7451        | 0.35  | 32000  | 0.7534          |
  | 0.6997        | 0.37  | 33000  | 0.7602          |
  | 0.8116        | 0.38  | 34000  | 0.7566          |
  | 0.7963        | 0.39  | 35000  | 0.7245          |
  | 0.786         | 0.4   | 36000  | 0.7311          |
  | 0.7991        | 0.41  | 37000  | 0.7230          |
  | 0.723         | 0.42  | 38000  | 0.7209          |
  | 0.789         | 0.43  | 39000  | 0.7418          |
  | 0.7296        | 0.44  | 40000  | 0.7325          |
  | 0.7363        | 0.45  | 41000  | 0.7134          |
  | 0.758         | 0.47  | 42000  | 0.6948          |
  | 0.711         | 0.48  | 43000  | 0.6992          |
  | 0.7984        | 0.49  | 44000  | 0.7055          |
  | 0.8402        | 0.5   | 45000  | 0.7108          |
  | 0.8553        | 0.51  | 46000  | 0.7005          |
  | 0.7538        | 0.52  | 47000  | 0.7208          |
  | 0.7169        | 0.53  | 48000  | 0.7291          |
  | 0.7345        | 0.54  | 49000  | 0.7195          |
  | 0.758         | 0.55  | 50000  | 0.6694          |
  | 0.7868        | 0.56  | 51000  | 0.6938          |
  | 0.6966        | 0.58  | 52000  | 0.6867          |
  | 0.7389        | 0.59  | 53000  | 0.6862          |
  | 0.7529        | 0.6   | 54000  | 0.7175          |
  | 0.7345        | 0.61  | 55000  | 0.6970          |
  | 0.766         | 0.62  | 56000  | 0.7017          |
  | 0.7043        | 0.63  | 57000  | 0.6916          |
  | 0.6474        | 0.64  | 58000  | 0.7129          |
  | 0.7456        | 0.65  | 59000  | 0.6802          |
  | 0.7512        | 0.66  | 60000  | 0.6951          |
  | 0.6816        | 0.68  | 61000  | 0.7072          |
  | 0.7206        | 0.69  | 62000  | 0.6967          |
  | 0.6439        | 0.7   | 63000  | 0.6798          |
  | 0.7309        | 0.71  | 64000  | 0.7163          |
  | 0.6925        | 0.72  | 65000  | 0.6794          |
  | 0.6833        | 0.73  | 66000  | 0.6637          |
  | 0.6643        | 0.74  | 67000  | 0.6855          |
  | 0.6433        | 0.75  | 68000  | 0.7035          |
  | 0.7595        | 0.76  | 69000  | 0.7008          |
  | 0.7214        | 0.78  | 70000  | 0.6618          |
  | 0.7111        | 0.79  | 71000  | 0.6850          |
  | 0.7375        | 0.8   | 72000  | 0.6909          |
  | 0.67