Text Classification
Transformers
Safetensors
English
deberta-v2
financial-sentiment
finance
sentiment-analysis
deberta-v3
Eval Results (legacy)
text-embeddings-inference
Instructions to use 9mark9/finbert-deberta-large-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 9mark9/finbert-deberta-large-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="9mark9/finbert-deberta-large-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("9mark9/finbert-deberta-large-sentiment") model = AutoModelForSequenceClassification.from_pretrained("9mark9/finbert-deberta-large-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm.model from 9mark9/finbert-deberta-large-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 2.46 MB
-
https://huggingface.co/9mark9/finbert-deberta-large-sentiment/resolve/main/spm.model
- Command line
-
hf download hf://9mark9/finbert-deberta-large-sentiment/spm.model
-
curl -L -o spm.model https://huggingface.co/9mark9/finbert-deberta-large-sentiment/resolve/main/spm.model
2.46 MB
- Xet hash:
- 6bd079d8585fbcdbf47cb3a534f888b5aaa32b8994301f5c47573e6cf825d7da
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.