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_mnli 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_mnli 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_mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_20_v2_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v2_mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_20_v2_mnli: direct link, hf CLI and curl.
- Browser
- Download file 479 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_20_v2_mnli/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_20_v2_mnli/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_20_v2_mnli/resolve/main/eval_results.json
479 Bytes
| { | |
| "epoch": 10.0, | |
| "epoch_mm": 10.0, | |
| "eval_accuracy": 0.6182373917473255, | |
| "eval_accuracy_mm": 0.6246948738812043, | |
| "eval_loss": 0.8496004343032837, | |
| "eval_loss_mm": 0.859828531742096, | |
| "eval_runtime": 2.821, | |
| "eval_runtime_mm": 2.8543, | |
| "eval_samples": 9815, | |
| "eval_samples_mm": 9832, | |
| "eval_samples_per_second": 3479.207, | |
| "eval_samples_per_second_mm": 3444.656, | |
| "eval_steps_per_second": 13.825, | |
| "eval_steps_per_second_mm": 13.664 | |
| } |