---
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
---

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# 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"](https://arxiv.org/abs/2403.05973) (ACL 2024).

## Model description

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/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