deberta v3 ft financial news sentiment analysis EU AI Act Compliance Profile

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 performs RoBERTa on a majority of NLU tasks with 80GB of 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 six 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.

Training and evaluation data

Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences f

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Tags

Library

Base Model