Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_100_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_100_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_100_v2_mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_100_v2_mrpc") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_100_v2_mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 309 Bytes
2731206 | 1 2 3 4 5 6 7 8 9 10 11 | {
"epoch": 7.0,
"eval_accuracy": 0.6838235294117647,
"eval_combined_score": 0.740336961556276,
"eval_f1": 0.7968503937007874,
"eval_loss": 0.5935776829719543,
"eval_runtime": 0.156,
"eval_samples": 408,
"eval_samples_per_second": 2614.887,
"eval_steps_per_second": 12.818
} |