Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_km_20_v2_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_km_20_v2_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_20_v2_stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_20_v2_stsb") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_20_v2_stsb") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 6.0, | |
| "eval_combined_score": 0.08975404205501787, | |
| "eval_loss": 2.251096725463867, | |
| "eval_pearson": 0.09287681409862332, | |
| "eval_runtime": 0.468, | |
| "eval_samples": 1500, | |
| "eval_samples_per_second": 3205.158, | |
| "eval_spearmanr": 0.08663127001141242, | |
| "eval_steps_per_second": 12.821 | |
| } |