README.md

metadata

license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
model-index:
  - name: apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125
    results: []
datasets:
  - stanfordnlp/coqa
library_name: transformers

apricot_binary_coqa_deberta-v3-base_for_gpt-3.5-turbo-0125

This model is fine-tuned for black-box LLM calibration as part of the 🍑 Apricot paper "Calibrating Large Language Models Using Their Generations Only" (ACL 2024).

Model description

This model is a fine-tuned version of microsoft/deberta-v3-base to predict the calibration score for the gpt-3.5-turbo-0125 model on the questions from the stanfordnlp/coqa dataset. It uses the binary type of calibration target score.

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: 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: 3.0

Framework versions

• Transformers 4.32.0
• Pytorch 2.0.0+cu117
• Datasets 2.14.6
• Tokenizers 0.13.3