Update README.md

README.md

@@ -3,9 +3,13 @@ license: mit
 base_model: microsoft/deberta-v3-base
 tags:
 - generated_from_trainer
+- calibration
+- uncertainty
 model-index:
 - name: apricot_binary_coqa_deberta-v3-base_for_vicuna-7b-v1.5
   results: []
+datasets:
+- stanfordnlp/coqa
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,11 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # apricot_binary_coqa_deberta-v3-base_for_vicuna-7b-v1.5
 
-This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the stanfordnlp/coqa dataset.
+
+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://github.com/parameterlab/apricot) (ACL 2024).
 
 ## Model description
 
-More information needed
+
+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 [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) model on the questions from the stanfordnlp/coqa dataset. It uses the binary type of calibration target score.
 
 ## Intended uses & limitations
 
@@ -31,6 +37,8 @@ More information needed
 
 ### Training hyperparameters
 
+**TODO**: update the values below
+
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
 - train_batch_size: 8
@@ -45,4 +53,4 @@ The following hyperparameters were used during training:
 - Transformers 4.32.0
 - Pytorch 2.0.0+cu117
 - Datasets 2.14.6
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3