Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_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_rand_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_rand_20_v2_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_20_v2_stsb") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v2_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Hartunka/tiny_bert_rand_20_v2_stsb: direct link, hf CLI and curl.
- Browser
- Download file 233 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_20_v2_stsb/resolve/main/train_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_20_v2_stsb/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Hartunka/tiny_bert_rand_20_v2_stsb/resolve/main/train_results.json
233 Bytes
| { | |
| "epoch": 6.0, | |
| "total_flos": 904528923227136.0, | |
| "train_loss": 1.9453966168389805, | |
| "train_runtime": 25.9633, | |
| "train_samples": 5749, | |
| "train_samples_per_second": 11071.387, | |
| "train_steps_per_second": 44.293 | |
| } |