Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v2_wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_5_v2_wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_wnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_wnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_5_v2_wnli: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_wnli/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v2_wnli/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_wnli/resolve/main/eval_results.json
228 Bytes
| { | |
| "epoch": 7.0, | |
| "eval_accuracy": 0.5352112676056338, | |
| "eval_loss": 0.6921765208244324, | |
| "eval_runtime": 0.0511, | |
| "eval_samples": 71, | |
| "eval_samples_per_second": 1390.658, | |
| "eval_steps_per_second": 19.587 | |
| } |