readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - null
  model_index:
  - name: topicalchat-multiturn
    results:
    - task:
        name: Causal Language Modeling
        type: text-generation
  ---
  
  <!-- 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. -->
  
  # topicalchat-multiturn
  
  This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.5260
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | No log        | 1.0   | 73   | 4.2992          |
  | No log        | 2.0   | 146  | 3.4433          |
  | No log        | 3.0   | 219  | 3.1606          |
  | No log        | 4.0   | 292  | 3.0366          |
  | No log        | 5.0   | 365  | 2.9679          |
  | No log        | 6.0   | 438  | 2.9131          |
  | 4.1401        | 7.0   | 511  | 2.8752          |
  | 4.1401        | 8.0   | 584  | 2.8391          |
  | 4.1401        | 9.0   | 657  | 2.8118          |
  | 4.1401        | 10.0  | 730  | 2.7871          |
  | 4.1401        | 11.0  | 803  | 2.7659          |
  | 4.1401        | 12.0  | 876  | 2.7489          |
  | 4.1401        | 13.0  | 949  | 2.7331          |
  | 2.9768        | 14.0  | 1022 | 2.7196          |
  | 2.9768        | 15.0  | 1095 | 2.7071          |
  | 2.9768        | 16.0  | 1168 | 2.6940          |
  | 2.9768        | 17.0  | 1241 | 2.6854          |
  | 2.9768        | 18.0  | 1314 | 2.6728          |
  | 2.9768        | 19.0  | 1387 | 2.6647          |
  | 2.9768        | 20.0  | 1460 | 2.6562          |
  | 2.7864        | 21.0  | 1533 | 2.6482          |
  | 2.7864        | 22.0  | 1606 | 2.6439          |
  | 2.7864        | 23.0  | 1679 | 2.6326          |
  | 2.7864        | 24.0  | 1752 | 2.6107          |
  | 2.7864        | 25.0  | 1825 | 2.6043          |
  | 2.7864        | 26.0  | 1898 | 2.5970          |
  | 2.7864        | 27.0  | 1971 | 2.5908          |
  | 2.6568        | 28.0  | 2044 | 2.5862          |
  | 2.6568        | 29.0  | 2117 | 2.5828          |
  | 2.6568        | 30.0  | 2190 | 2.5765          |
  | 2.6568        | 31.0  | 2263 | 2.5742          |
  | 2.6568        | 32.0  | 2336 | 2.5682          |
  | 2.6568        | 33.0  | 2409 | 2.5656          |
  | 2.6568        | 34.0  | 2482 | 2.5614          |
  | 2.5489        | 35.0  | 2555 | 2.5605          |
  | 2.5489        | 36.0  | 2628 | 2.5552          |
  | 2.5489        | 37.0  | 2701 | 2.5541          |
  | 2.5489        | 38.0  | 2774 | 2.5494          |
  | 2.5489        | 39.0  | 2847 | 2.5491          |
  | 2.5489        | 40.0  | 2920 | 2.5455          |
  | 2.5489        | 41.0  | 2993 | 2.5452          |
  | 2.475         | 42.0  | 3066 | 2.5433          |
  | 2.475         | 43.0  | 3139 | 2.5397          |
  | 2.475         | 44.0  | 3212 | 2.5386          |
  | 2.475         | 45.0  | 3285 | 2.5400          |
  | 2.475         | 46.0  | 3358 | 2.5339          |
  | 2.475         | 47.0  | 3431 | 2.5327          |
  | 2.4144        | 48.0  | 3504 | 2.5327          |
  | 2.4144        | 49.0  | 3577 | 2.5312          |
  | 2.4144        | 50.0  | 3650 | 2.5338          |
  | 2.4144        | 51.0  | 3723 | 2.5314          |
  | 2.4144        | 52.0  | 3796 | 2.5309          |
  | 2.4144        | 53.0  | 3869 | 2.5289          |
  | 2.4144        | 54.0  | 3942 | 2.5290          |
  | 2.3642        | 55.0  | 4015 | 2.5270          |
  | 2.3642        | 56.0  | 4088 | 2.5270          |
  | 2.3642        | 57.0  | 4161 | 2.5263          |
  | 2.3642        | 58.0  | 4234 | 2.5267          |
  | 2.3642        | 59.0  | 4307 | 2.5273          |
  | 2.3642        | 60.0  | 4380 | 2.5258          |
  | 2.3642        | 61.0  | 4453 | 2.5253          |
  | 2.3216        | 62.0  | 4526 | 2.5244          |
  | 2.3216        | 63.0  | 4599 | 2.5256          |
  | 2.3216        | 64.0  | 4672 | 2.5227          |
  | 2.3216        | 65.0  | 4745 | 2.5241          |
  | 2.3216        | 66.0  | 4818 | 2.5244          |
  | 2.3216        | 67.0  | 4891 | 2.5236          |
  | 2.3216        | 68.0  | 4964 | 2.5251          |
  | 2.2879        | 69.0  | 5037 | 2.5231          |
  | 2.2879        | 70.0  | 5110 | 2.5254          |
  | 2.2879        | 71.0  | 5183 | 2.5242          |
  | 2.2879        | 72.0  | 5256 | 2.5254          |
  | 2.2879        | 73.0  | 5329 | 2.5253          |
  | 2.2879        | 74.0  | 5402 | 2.