Text Classification
Transformers
Safetensors
English
roberta
economics
finance
bert
language-model
financial-nlp
economic-analysis
Instructions to use beethogedeon/SentEconBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beethogedeon/SentEconBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="beethogedeon/SentEconBert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("beethogedeon/SentEconBert") model = AutoModelForSequenceClassification.from_pretrained("beethogedeon/SentEconBert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f219e6e7755b22a9a1b5fcb6c8467301645494132061be8137dfb2f0ca58aab9
- Size of remote file:
- 330 MB
- SHA256:
- 59c596d1a1e83b1a683ad9dff5ede4abbb60a21ffef98a56fad1d6b8ddeeb205
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.