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
Download eval_report.json from davidburhans/gevva-e2b: direct link, hf CLI and curl.
- Browser
- Download file 515 Bytes
-
https://huggingface.co/davidburhans/gevva-e2b/resolve/main/eval_report.json
- Command line
-
hf download hf://davidburhans/gevva-e2b/eval_report.json
-
curl -L -o eval_report.json https://huggingface.co/davidburhans/gevva-e2b/resolve/main/eval_report.json
515 Bytes
| { | |
| "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 | |
| } |