Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_km_5_v2_mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_km_5_v2_mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_km_5_v2_mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_km_5_v2_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_km_5_v2_mnli") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 8.0, | |
| "epoch_mm": 8.0, | |
| "eval_accuracy": 0.6837493632195619, | |
| "eval_accuracy_mm": 0.684499593165175, | |
| "eval_loss": 0.7321322560310364, | |
| "eval_loss_mm": 0.722257673740387, | |
| "eval_runtime": 6.1094, | |
| "eval_runtime_mm": 6.1393, | |
| "eval_samples": 9815, | |
| "eval_samples_mm": 9832, | |
| "eval_samples_per_second": 1606.552, | |
| "eval_samples_per_second_mm": 1601.488, | |
| "eval_steps_per_second": 6.384, | |
| "eval_steps_per_second_mm": 6.353 | |
| } |