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_mnli 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_mnli 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_mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_5_v2_mnli: direct link, hf CLI and curl.
- Browser
- Download file 481 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_mnli/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v2_mnli/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_mnli/resolve/main/eval_results.json
481 Bytes
| { | |
| "epoch": 10.0, | |
| "epoch_mm": 10.0, | |
| "eval_accuracy": 0.6164034640855833, | |
| "eval_accuracy_mm": 0.6216436126932465, | |
| "eval_loss": 0.8499380350112915, | |
| "eval_loss_mm": 0.8520750999450684, | |
| "eval_runtime": 2.8358, | |
| "eval_runtime_mm": 2.8349, | |
| "eval_samples": 9815, | |
| "eval_samples_mm": 9832, | |
| "eval_samples_per_second": 3461.094, | |
| "eval_samples_per_second_mm": 3468.252, | |
| "eval_steps_per_second": 13.753, | |
| "eval_steps_per_second_mm": 13.757 | |
| } |