metadata

license: mit
base_model: microsoft/deberta-v3-small
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - accuracy
  - f1
model-index:
  - name: deberta-v3-ft-news-sentiment-analisys
    results: []

deberta-v3-ft-news-sentiment-analisys

This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0233
Precision: 0.9940
Recall: 0.9940
Accuracy: 0.9940
F1: 0.9940

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: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Accuracy	F1
No log	1.0	214	0.1865	0.9323	0.9323	0.9323	0.9323
No log	2.0	428	0.0742	0.9771	0.9771	0.9771	0.9771
0.2737	3.0	642	0.0479	0.9855	0.9855	0.9855	0.9855
0.2737	4.0	856	0.0284	0.9923	0.9923	0.9923	0.9923
0.0586	5.0	1070	0.0233	0.9940	0.9940	0.9940	0.9940

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Citation

@misc {manuel_romero_2024,
    author       = { {Manuel Romero} },
    title        = { deberta-v3-ft-financial-news-sentiment-analysis (Revision 7430ace) },
    year         = 2024,
    url          = { https://huggingface.co/mrm8488/deberta-v3-ft-financial-news-sentiment-analysis },
    doi          = { 10.57967/hf/1666 },
    publisher    = { Hugging Face }
}