Text Classification
Transformers
Safetensors
modernbert
finance
financial-sentiment
sentiment-analysis
multilingual
financial-news
fintech
trading
market-sentiment
mmbert
cross-lingual
text-embeddings-inference
Instructions to use Kenpache/finbert-multilingual-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kenpache/finbert-multilingual-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kenpache/finbert-multilingual-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kenpache/finbert-multilingual-v2") model = AutoModelForSequenceClassification.from_pretrained("Kenpache/finbert-multilingual-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Kenpache/finbert-multilingual-v2: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/Kenpache/finbert-multilingual-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Kenpache/finbert-multilingual-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Kenpache/finbert-multilingual-v2/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- bcba6d32a90f13b3acf5db3fd4601fb75226d510bdd9c40cc31f6ef25a94d505
- Size of remote file:
- 34.4 MB
- SHA256:
- e90e95ed27d5922da898753b661bdd5d3e0e8de3a4f86f80a9b22728e7e5523e
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