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")# pip install -U transformers accelerate # 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
Download tokenizer.json from Imad17700/sec-bert-causal-classifier_s2: direct link, hf CLI and curl.
- Browser
- Download file 8.34 MB
-
https://huggingface.co/Imad17700/sec-bert-causal-classifier_s2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Imad17700/sec-bert-causal-classifier_s2/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Imad17700/sec-bert-causal-classifier_s2/resolve/main/tokenizer.json
8.34 MB
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