Text Classification
Transformers
PyTorch
Safetensors
English
bert
finance
financial
news
sentiment-analysis
finbert
transfomer
financial-news
financial-news-sentiment
text-embeddings-inference
Instructions to use project-aps/finbert-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use project-aps/finbert-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="project-aps/finbert-finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("project-aps/finbert-finetune") model = AutoModelForSequenceClassification.from_pretrained("project-aps/finbert-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from project-aps/finbert-finetune: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/project-aps/finbert-finetune/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://project-aps/finbert-finetune@refs/pr/2/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/project-aps/finbert-finetune/resolve/refs%2Fpr%2F2/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.