Text Classification
Transformers
Safetensors
English
bert
finbert
finance
sentiment
sentiment-analysis
financial-sentiment
text-embeddings-inference
Instructions to use ENTUM-AI/FinBERT-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ENTUM-AI/FinBERT-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ENTUM-AI/FinBERT-Pro")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ENTUM-AI/FinBERT-Pro") model = AutoModelForSequenceClassification.from_pretrained("ENTUM-AI/FinBERT-Pro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b2f41c83d0e84d2f16a2fe806879659d9619116f57dd4abf90ac07005294d7e
- Size of remote file:
- 438 MB
- SHA256:
- eb7830bd4035c8bb5c7b4aed55b9bf3cf290cb21adcd3c34feab7f269d0c9618
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