Finance Sentiment
Collection
A collections of models for detecting financial sentiment. • 8 items • Updated • 1
Text Classification
Transformers
PyTorch
Safetensors
Polish
bert
financial-sentiment-analysis
sentiment-analysis
herbert
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bardsai/finance-sentiment-pl-base")
model = AutoModelForSequenceClassification.from_pretrained("bardsai/finance-sentiment-pl-base")Quick Links
Finance Sentiment PL (base)
Finance Sentiment PL (base) is a model based on herbert-base for analyzing sentiment of Polish 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-pl-base")
nlp("Sprzedaż netto wzrosła o 30% do 36 mln EUR.")
[{'label': 'positive', 'score': 0.9999998807907104}]
Performance
| Metric | Value |
|---|---|
| f1 macro | 0.969 |
| precision macro | 0.971 |
| recall macro | 0.968 |
| accuracy | 0.976 |
| samples per second | 136.8 |
(The performance was evaluated on RTX 3090 gpu)
Changelog
- 2022-12-01: Rename the model to finance-sentiment-pl-base
- 2022-11-15: Initial release
License
This model is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, inherited from the base model allegro/herbert-base-cased (also CC BY 4.0).
Attribution: HerBERT — Allegro ML Research and the Linguistic Engineering Group at the Institute of Computer Science, Polish Academy of Sciences; Finance Sentiment PL (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-pl-base
Base model
allegro/herbert-base-casedDataset used to train bardsai/finance-sentiment-pl-base
Collection including bardsai/finance-sentiment-pl-base
Evaluation results
- F1 (macro) on Financial PhraseBank (translated to Polish)self-reported0.969
- Precision (macro) on Financial PhraseBank (translated to Polish)self-reported0.971
- Recall (macro) on Financial PhraseBank (translated to Polish)self-reported0.968
- Accuracy on Financial PhraseBank (translated to Polish)self-reported0.976
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bardsai/finance-sentiment-pl-base")