metadata

license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
  - calibration
  - uncertainty
model-index:
  - name: apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5
    results: []
datasets:
  - stanfordnlp/coqa

apricot_clustering_coqa_deberta-v3-base_for_vicuna-7b-v1.5

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 lmsys/vicuna-7b-v1.5 model on the questions from the stanfordnlp/coqa dataset. It uses the clustering type of calibration target score.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

TODO: update the values below

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