Text Classification
Transformers
Safetensors
English
deberta-v2
financial-nlp
causal-detection
deberta
sequence-classification
finance
sec-filings
text-embeddings-inference
Instructions to use Imad17700/sec-bert-causal-classifier_s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Imad17700/sec-bert-causal-classifier_s2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Imad17700/sec-bert-causal-classifier_s2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Imad17700/sec-bert-causal-classifier_s2") model = AutoModelForSequenceClassification.from_pretrained("Imad17700/sec-bert-causal-classifier_s2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9985b8920a68c9abdcb1651bba5e78bbc5f764f02e97c0e0733fea2e845512cc
- Size of remote file:
- 1.74 GB
- SHA256:
- c55fdcfc498bfb2fcdf70a2fd05f5a765c50fdd19e0ff8d1e43874e6632f4b04
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.