Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_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_rand_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_rand_5_v2_mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_5_v2_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_5_v2_mnli") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 10.0, | |
| "total_flos": 5.166258268431053e+17, | |
| "train_loss": 0.6249743345974321, | |
| "train_runtime": 6473.467, | |
| "train_samples": 392702, | |
| "train_samples_per_second": 3033.166, | |
| "train_steps_per_second": 11.848 | |
| } |