Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_10_v1_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_10_v1_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_10_v1_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v1_stsb") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v1_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_10_v1_stsb: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_10_v1_stsb/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_10_v1_stsb/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_10_v1_stsb/resolve/main/eval_results.json
321 Bytes
| { | |
| "epoch": 6.0, | |
| "eval_combined_score": 0.07619254605764625, | |
| "eval_loss": 2.3236286640167236, | |
| "eval_pearson": 0.07898484684438345, | |
| "eval_runtime": 0.4822, | |
| "eval_samples": 1500, | |
| "eval_samples_per_second": 3110.542, | |
| "eval_spearmanr": 0.07340024527090903, | |
| "eval_steps_per_second": 12.442 | |
| } |