Text Classification
Transformers
Safetensors
PyTorch
xlm-roberta
finance
sentiment-analysis
financial-sentiment-analysis
multilingual
financial-nlp
perspective-aware
stock-market
text-embeddings-inference
Instructions to use Kenpache/flame2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kenpache/flame2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kenpache/flame2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kenpache/flame2") model = AutoModelForSequenceClassification.from_pretrained("Kenpache/flame2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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# FLAME2 — Financial Language Analysis for Multilingual Economics v2
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**One model. Ten languages. 150,000 headlines. Perspective-aware financial sentiment.**
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---
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license: apache-2.0
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language:
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- ar
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- de
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- en
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- es
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- fr
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- hi
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- ja
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- ko
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- pt
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- zh
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tags:
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- finance
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- sentiment-analysis
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- multilingual
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- xlm-roberta
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- financial-nlp
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- perspective-aware
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- stock-market
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- text-classification
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metrics:
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- accuracy
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- f1
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pipeline_tag: text-classification
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---
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# FLAME2 — Financial Language Analysis for Multilingual Economics v2
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**One model. Ten languages. 150,000 headlines. Perspective-aware financial sentiment.**
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