metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: distilbert-base-uncased-xsum-factuality
    results: []
datasets:
  - xsum_factuality
language:
  - en

Distilbert-base-uncased-xsum-factuality

This model is a fine-tuned version of distilbert-base-uncased on the XSum-Factuality dataset. You can view more implementation details as part of this GitHub repository. It achieves the following results on the evaluation set:

Loss: 0.6850
Accuracy: 0.6332
F1: 0.6212
Precision: 0.6526
Recall: 0.6332

Weights and Biases Documentation

View the full run on Weights & Biases

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: 1e-06
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6904	6.93	1040	0.6850	0.6332	0.6212	0.6526	0.6332

Framework versions

Transformers 4.35.0
Pytorch 2.0.1
Datasets 2.14.6
Tokenizers 0.14.1