Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_100_v2_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_100_v2_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_100_v2_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_100_v2_stsb") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_100_v2_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 320 Bytes
477368e | 1 2 3 4 5 6 7 8 9 10 11 | {
"epoch": 8.0,
"eval_combined_score": 0.18249862203222653,
"eval_loss": 2.357128858566284,
"eval_pearson": 0.19035307550832772,
"eval_runtime": 0.4622,
"eval_samples": 1500,
"eval_samples_per_second": 3245.315,
"eval_spearmanr": 0.17464416855612533,
"eval_steps_per_second": 12.981
} |