language:
- fr
license: mit
library_name: transformers
pipeline_tag: text-classification
tags:
- text-classification
- financial-sentiment-analysis
- sentiment-analysis
- camembert
base_model: camembert-base
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: >-
Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions
d'euros.
example_title: Example 1
- text: Coup d'envoi du vendredi fou. Liste des promotions en magasin.
example_title: Example 2
- text: >-
Les actions de CDPROJEKT ont enregistré la plus forte baisse parmi les
entreprises cotées au WSE.
example_title: Example 3
model-index:
- name: finance-sentiment-fr-base
results:
- task:
type: text-classification
name: Financial Sentiment Analysis
dataset:
name: Financial PhraseBank (translated to French)
type: financial_phrasebank
metrics:
- type: f1
value: 0.963
name: F1 (macro)
- type: precision
value: 0.959
name: Precision (macro)
- type: recall
value: 0.967
name: Recall (macro)
- type: accuracy
value: 0.971
name: Accuracy
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