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 test_evaluation_report.json from davidburhans/gevva-e2b: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/davidburhans/gevva-e2b/resolve/main/test_evaluation_report.json
- Command line
-
hf download hf://davidburhans/gevva-e2b/test_evaluation_report.json
-
curl -L -o test_evaluation_report.json https://huggingface.co/davidburhans/gevva-e2b/resolve/main/test_evaluation_report.json
1.59 kB
| { | |
| "test_primary": { | |
| "data_path": "data/test.jsonl", | |
| "n_samples": 3113, | |
| "accuracy": 0.8233, | |
| "ece": 0.0175, | |
| "brier": 0.249, | |
| "per_class": { | |
| "contradiction": { | |
| "precision": 0.7712, | |
| "recall": 0.9038, | |
| "f1": 0.8323, | |
| "support": 884 | |
| }, | |
| "entailment": { | |
| "precision": 0.8507, | |
| "recall": 0.8219, | |
| "f1": 0.836, | |
| "support": 1061 | |
| }, | |
| "neutral": { | |
| "precision": 0.8479, | |
| "recall": 0.7637, | |
| "f1": 0.8036, | |
| "support": 1168 | |
| } | |
| }, | |
| "language_accuracy": { | |
| "ar": 0.8788, | |
| "de": 0.7273, | |
| "en": 0.8345, | |
| "es": 0.7963, | |
| "fr": 0.8065, | |
| "hi": 0.7143, | |
| "ru": 0.72, | |
| "sw": 0.5556, | |
| "vi": 0.5926, | |
| "zh": 0.6216 | |
| } | |
| }, | |
| "test_v2": { | |
| "data_path": "data/test_v2.jsonl", | |
| "n_samples": 3113, | |
| "accuracy": 0.8195, | |
| "ece": 0.0184, | |
| "brier": 0.2554, | |
| "per_class": { | |
| "contradiction": { | |
| "precision": 0.7558, | |
| "recall": 0.9073, | |
| "f1": 0.8246, | |
| "support": 863 | |
| }, | |
| "entailment": { | |
| "precision": 0.8507, | |
| "recall": 0.8219, | |
| "f1": 0.836, | |
| "support": 1061 | |
| }, | |
| "neutral": { | |
| "precision": 0.8517, | |
| "recall": 0.7536, | |
| "f1": 0.7996, | |
| "support": 1189 | |
| } | |
| }, | |
| "language_accuracy": { | |
| "ar": 0.8788, | |
| "de": 0.7273, | |
| "en": 0.8302, | |
| "es": 0.7963, | |
| "fr": 0.8065, | |
| "hi": 0.7143, | |
| "ru": 0.72, | |
| "sw": 0.5556, | |
| "vi": 0.5926, | |
| "zh": 0.6216 | |
| } | |
| } | |
| } |