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_mrpc 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_mrpc 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_mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mrpc") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Hartunka/tiny_bert_rand_5_v2_mrpc: direct link, hf CLI and curl.
- Browser
- Download file 311 Bytes
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_mrpc/resolve/main/eval_results.json
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v2_mrpc/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_mrpc/resolve/main/eval_results.json
311 Bytes
| { | |
| "epoch": 7.0, | |
| "eval_accuracy": 0.6985294117647058, | |
| "eval_combined_score": 0.7521083077780875, | |
| "eval_f1": 0.8056872037914692, | |
| "eval_loss": 0.5934136509895325, | |
| "eval_runtime": 0.1533, | |
| "eval_samples": 408, | |
| "eval_samples_per_second": 2660.765, | |
| "eval_steps_per_second": 13.043 | |
| } |