Text Classification
Transformers
Safetensors
xlm-roberta
finance
financial-sentiment
sentiment-analysis
multilingual
financial-news
fintech
trading
market-sentiment
cross-lingual
text-embeddings-inference
Instructions to use Kenpache/finbert-multilingual-v2-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kenpache/finbert-multilingual-v2-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kenpache/finbert-multilingual-v2-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kenpache/finbert-multilingual-v2-large") model = AutoModelForSequenceClassification.from_pretrained("Kenpache/finbert-multilingual-v2-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04376a1b787bda3157c89ca26c28e853b3b482e2e5e50bda9fe0397db683afca
- Size of remote file:
- 17.1 MB
- SHA256:
- 19ffc61f7cb57aa8c419fa9d2409c2af1abf4a92ba5a12c60a09cafa5a59c9b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.