Text Classification
Transformers
PyTorch
Safetensors
French
camembert
financial-sentiment-analysis
sentiment-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use bardsai/finance-sentiment-fr-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bardsai/finance-sentiment-fr-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/finance-sentiment-fr-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bardsai/finance-sentiment-fr-base") model = AutoModelForSequenceClassification.from_pretrained("bardsai/finance-sentiment-fr-base") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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language: fr
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tags:
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- text-classification
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- financial-sentiment-analysis
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- sentiment-analysis
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datasets:
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- datasets/financial_phrasebank
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metrics:
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- f1
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- accuracy
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- precision
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- recall
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widget:
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- text: "Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros."
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example_title: "Example 1"
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- text: "Coup d'envoi du vendredi fou. Liste des promotions en magasin."
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example_title: "Example 2"
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- text: "Les actions de CDPROJEKT ont enregistré la plus forte baisse parmi les entreprises cotées au WSE."
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example_title: "Example 3"
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---
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# Finance Sentiment FR (base)
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Finance Sentiment FR (base) is a model based on [camembert-base](https://huggingface.co/camembert-base) for analyzing sentiment of French financial news. It was trained on the translated version of [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (20014) for 10 epochs on single RTX3090 gpu.
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The model will give you a three labels: positive, negative and neutral.
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## How to use
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You can use this model directly with a pipeline for sentiment-analysis:
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```python
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from transformers import pipeline
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nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-fr-base")
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nlp("Le chiffre d'affaires net a augmenté de 30 % pour atteindre 36 millions d'euros.")
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```
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```bash
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[{'label': 'positive', 'score': 0.9987998807375955}]
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```
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## Performance
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| Metric | Value |
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| --- | ----------- |
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| f1 macro | 0.963 |
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| precision macro | 0.959 |
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| recall macro | 0.967 |
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| accuracy | 0.971 |
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| samples per second | 140.8 |
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(The performance was evaluated on RTX 3090 gpu)
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## Changelog
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- 2023-09-18: Initial release
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## About bards.ai
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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](https://bards.ai/)
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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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