Text Classification
Transformers
Safetensors
English
deberta-v2
scientific-claim-verification
scifact
evidence-aware-verification
retrieval-aware-training
text-embeddings-inference
Instructions to use rishhh/verisci-claim-verifier-dense-adapted-seed123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishhh/verisci-claim-verifier-dense-adapted-seed123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rishhh/verisci-claim-verifier-dense-adapted-seed123")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rishhh/verisci-claim-verifier-dense-adapted-seed123") model = AutoModelForSequenceClassification.from_pretrained("rishhh/verisci-claim-verifier-dense-adapted-seed123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5bbac5f1dc548b969f8f7393739bca6393e140d06aaf7ce3dcdf7cc29f31a54
- Size of remote file:
- 5.91 kB
- SHA256:
- f4a3a9de5c9288ff326de5d004354d5feb2ff5650e3d7659adc1cdab843495c2
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