README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - scitldr
model-index:
  - name: distilbart-cnn-12-6-finetuned-scitldr
    results: []
widget:
  - text: >-
      Reinforcement learning provides a powerful and general framework for
      decision making and control, but its application in practice is often
      hindered by the need for extensive feature and reward engineering. Deep
      reinforcement learning methods can remove the need for explicit
      engineering of policy or value features but still require a manually
      specified reward function. Inverse reinforcement learning holds the
      promise of automatic reward acquisition, but has proven exceptionally
      difficult to apply to large, high-dimensional problems with unknown
      dynamics. In this work, we propose AIRL, a practical and scalable inverse
      reinforcement learning algorithm based on an adversarial reward learning
      formulation that is competitive with direct imitation learning algorithms.
      Additionally, we show that AIRL is able to recover portable reward
      functions that are robust to changes in dynamics, enabling us to learn
      policies even under significant variation in the environment seen during
      training. 

distilbart-cnn-12-6-finetuned-scitldr

This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the scitldr dataset. It achieves the following results on the evaluation set:

• eval_loss: 3.7113
• eval_rouge1: 31.4431
• eval_rouge2: 13.1766
• eval_rougeL: 24.2038
• eval_rougeLsum: 26.3167
• eval_runtime: 151.7265
• eval_samples_per_second: 4.08
• eval_steps_per_second: 0.514
• epoch: 4.0
• step: 996

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: 5.6e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1