metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
base_model: sileod/deberta-v3-base-tasksource-nli
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: deberta-v3-bass-complex-questions_classifier
    results: []
widget:
  - text: Why did the company decide to enter the Latin America region?
    example_title: Simple Query
  - text: >-
      What was the Company's net profit margin in the last fiscal year, and how
      does it compare to the industry average?
    example_title: Multiple Queries
  - text: >-
      Compare the customer growth rates in the SaaS sector of CloudServices Inc.
      with that of SaaSSolutions Tech over the last two years.
    example_title: Comparable Query
  - text: What are your favorite ways to show friends you're thinking of them?
    example_title: SmallTalk Query
  - text: Alter the proposal to emphasize sustainability practices.
    example_title: Functional Query

deberta-v3-bass-complex-questions_classifier

This model is a fine-tuned version of sileod/deberta-v3-base-tasksource-nli on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0001
Accuracy: 1.0
Precision: 1.0
Recall: 1.0
F1: 1.0

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 0
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0532	2.3585	500	0.0001	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.41.2
Pytorch 2.1.1
Datasets 2.15.0
Tokenizers 0.19.1