Instructions to use Mardiyyah/no_vague_no_downsample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mardiyyah/no_vague_no_downsample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Mardiyyah/no_vague_no_downsample")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Mardiyyah/no_vague_no_downsample") model = AutoModelForTokenClassification.from_pretrained("Mardiyyah/no_vague_no_downsample") - Notebooks
- Google Colab
- Kaggle
File size: 466 Bytes
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"epoch": 4.455445544554456,
"eval_accuracy": 0.9815417166677193,
"eval_f1": 0.7460203642621539,
"eval_loss": 0.07425664365291595,
"eval_precision": 0.712798026856673,
"eval_recall": 0.7824909747292419,
"eval_samples": 2614,
"total_flos": 760697313381126.0,
"train_loss": 0.1482748039563497,
"train_runtime": 320.0844,
"train_samples": 6453,
"train_samples_per_second": 403.206,
"train_steps_per_second": 12.622
} |