Instructions to use avinasht/finbert_roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avinasht/finbert_roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avinasht/finbert_roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avinasht/finbert_roberta-base") model = AutoModelForSequenceClassification.from_pretrained("avinasht/finbert_roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c52c3257b20984317785b581d9ffd6d916d161486773f9c1e878d8b79e55c62
- Size of remote file:
- 4.66 kB
- SHA256:
- 5679de29ba5f61809913f83d61399a8e5c236dc7a5caae4d5cbc2712fb06a721
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.