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-small-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:
| Metric | Value |
|---|---|
| F1 | 0.9940 |
| Accuracy | 0.9940 |
| Precision | 0.9940 |
| Recall | 0.9940 |
| Loss | 0.0233 |
Model description
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper.
Please check the official repository for more implementation details and updates.
The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. It has 44M backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
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 }
}