Finance Sentiment
Collection
A collections of models for detecting financial sentiment. • 8 items • Updated • 1
Finance Sentiment FR (base)
Finance Sentiment FR (base) is a model based on camembert-base for analyzing sentiment of French financial news. It was trained on the translated version of Financial PhraseBank by Malo et al. (2014) for 10 epochs on single RTX3090 gpu.
The model will give you a three labels: positive, negative and neutral.
How to use
You can use this model directly with a pipeline for sentiment-analysis:
from transformers import pipeline
nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-fr-base")
nlp("Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros.")
[{'label': 'positive', 'score': 0.9987998807375955}]
Performance
| Metric | Value |
|---|---|
| f1 macro | 0.963 |
| precision macro | 0.959 |
| recall macro | 0.967 |
| accuracy | 0.971 |
| samples per second | 140.8 |
(The performance was evaluated on RTX 3090 gpu)
Changelog
- 2023-09-18: Initial release
License
This model is released under the MIT License, inherited from the base model camembert-base (MIT).
Attribution: CamemBERT — Inria, Facebook AI Research; Finance Sentiment FR (base) — bards.ai.
About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai
Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai
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Model tree for bardsai/finance-sentiment-fr-base
Base model
almanach/camembert-baseDataset used to train bardsai/finance-sentiment-fr-base
Collection including bardsai/finance-sentiment-fr-base
Evaluation results
- F1 (macro) on Financial PhraseBank (translated to French)self-reported0.963
- Precision (macro) on Financial PhraseBank (translated to French)self-reported0.959
- Recall (macro) on Financial PhraseBank (translated to French)self-reported0.967
- Accuracy on Financial PhraseBank (translated to French)self-reported0.971