Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v1_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_v1_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_v1_mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v1_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v1_mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_5_v1_mnli: direct link, hf CLI and curl.
- Browser
- Download file 478 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_mnli/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v1_mnli/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_mnli/resolve/main/eval_results.json
478 Bytes
| { | |
| "epoch": 9.0, | |
| "epoch_mm": 9.0, | |
| "eval_accuracy": 0.6387162506367804, | |
| "eval_accuracy_mm": 0.6433075671277462, | |
| "eval_loss": 0.8246227502822876, | |
| "eval_loss_mm": 0.813347578048706, | |
| "eval_runtime": 2.8802, | |
| "eval_runtime_mm": 2.9332, | |
| "eval_samples": 9815, | |
| "eval_samples_mm": 9832, | |
| "eval_samples_per_second": 3407.732, | |
| "eval_samples_per_second_mm": 3351.944, | |
| "eval_steps_per_second": 13.541, | |
| "eval_steps_per_second_mm": 13.296 | |
| } |