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_sst2 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_sst2 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_sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v1_sst2") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v1_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_5_v1_sst2: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_sst2/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v1_sst2/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_sst2/resolve/main/eval_results.json
227 Bytes
| { | |
| "epoch": 6.0, | |
| "eval_accuracy": 0.8142201834862385, | |
| "eval_loss": 0.4533984661102295, | |
| "eval_runtime": 0.2799, | |
| "eval_samples": 872, | |
| "eval_samples_per_second": 3115.1, | |
| "eval_steps_per_second": 14.289 | |
| } |