Text Classification
Transformers
Safetensors
gemma4
gevva
cross-encoder
nli
gemma-4
system1
decision-engine
fast-inference
multimodal
vision
long-context
128k
zero-shot
tool-routing
reranking
hallucination-detection
Eval Results (legacy)
Instructions to use davidburhans/gevva-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidburhans/gevva-e2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davidburhans/gevva-e2b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("davidburhans/gevva-e2b") model = AutoModelForSequenceClassification.from_pretrained("davidburhans/gevva-e2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 515 Bytes
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"accuracy": 0.8285,
"brier": 0.2475,
"ece": 0.025,
"n_samples": 2922,
"per_class": {
"contradiction": {
"precision": 0.8512,
"recall": 0.8667,
"f1": 0.8589,
"support": 990
},
"entailment": {
"precision": 0.8309,
"recall": 0.8105,
"f1": 0.8206,
"support": 1103
},
"neutral": {
"precision": 0.7983,
"recall": 0.807,
"f1": 0.8026,
"support": 829
}
},
"decision_accuracy": 0.7,
"anchor_accuracy": 0.8172
} |