Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_km_100_v2_sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_km_100_v2_sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_100_v2_sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_100_v2_sst2") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_100_v2_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_km_100_v2_sst2: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_km_100_v2_sst2/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_km_100_v2_sst2/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_km_100_v2_sst2/resolve/main/eval_results.json
230 Bytes
| { | |
| "epoch": 6.0, | |
| "eval_accuracy": 0.8027522935779816, | |
| "eval_loss": 0.46620461344718933, | |
| "eval_runtime": 0.2858, | |
| "eval_samples": 872, | |
| "eval_samples_per_second": 3050.835, | |
| "eval_steps_per_second": 13.995 | |
| } |