Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v2_mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_5_v2_mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 481 Bytes
33774b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"epoch": 10.0,
"epoch_mm": 10.0,
"eval_accuracy": 0.6164034640855833,
"eval_accuracy_mm": 0.6216436126932465,
"eval_loss": 0.8499380350112915,
"eval_loss_mm": 0.8520750999450684,
"eval_runtime": 2.8358,
"eval_runtime_mm": 2.8349,
"eval_samples": 9815,
"eval_samples_mm": 9832,
"eval_samples_per_second": 3461.094,
"eval_samples_per_second_mm": 3468.252,
"eval_steps_per_second": 13.753,
"eval_steps_per_second_mm": 13.757
} |