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-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidburhans/gevva-e4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davidburhans/gevva-e4b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("davidburhans/gevva-e4b") model = AutoModelForSequenceClassification.from_pretrained("davidburhans/gevva-e4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 520 Bytes
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"accuracy": 0.8054,
"brier": 0.2725,
"ece": 0.0245,
"n_samples": 2914,
"per_class": {
"contradiction": {
"precision": 0.8294,
"recall": 0.8512,
"f1": 0.8402,
"support": 988
},
"entailment": {
"precision": 0.8074,
"recall": 0.7884,
"f1": 0.7978,
"support": 1101
},
"neutral": {
"precision": 0.7733,
"recall": 0.7733,
"f1": 0.7733,
"support": 825
}
},
"decision_accuracy": 0.7092,
"anchor_accuracy": 0.8104
} |