Text Classification
Transformers
Safetensors
llama
multi-label
question-answering
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use saiteki-kai/QA-Llama-3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saiteki-kai/QA-Llama-3.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saiteki-kai/QA-Llama-3.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("saiteki-kai/QA-Llama-3.1") model = AutoModelForSequenceClassification.from_pretrained("saiteki-kai/QA-Llama-3.1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_accuracy": 0.6834501137860821, | |
| "test_flagged/accuracy": 0.8485147921906815, | |
| "test_flagged/f1": 0.8584934687141619, | |
| "test_flagged/precision": 0.8994256241941155, | |
| "test_flagged/recall": 0.8211247257745198, | |
| "test_loss": 0.08345632255077362, | |
| "test_macro_f1": 0.6200746051485584, | |
| "test_macro_precision": 0.7147873439119878, | |
| "test_macro_recall": 0.5715412667131191, | |
| "test_micro_f1": 0.7434023275005474, | |
| "test_micro_precision": 0.7991924971031287, | |
| "test_micro_recall": 0.6948931078996253, | |
| "test_runtime": 90.6304, | |
| "test_samples_per_second": 736.971, | |
| "test_steps_per_second": 5.76 | |
| } |