Instructions to use karths/binary_classification_train_documentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_documentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_documentation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_documentation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_documentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "0": { | |
| "eval_loss": 0.3270025849342346, | |
| "eval_precision": 0.8839922332569391, | |
| "eval_recall": 0.8904689378757515, | |
| "eval_acc": 0.8869017107708079, | |
| "eval_mcc": 0.7738253985011632, | |
| "eval_f1": 0.8872187657239611, | |
| "eval_auc": 0.9530052709250063, | |
| "eval_runtime": 225.8649, | |
| "eval_samples_per_second": 552.791, | |
| "eval_steps_per_second": 8.638, | |
| "epoch": 5.0 | |
| }, | |
| "1": { | |
| "eval_loss": 0.22230181097984314, | |
| "eval_precision": 0.929331657571949, | |
| "eval_recall": 0.9365130260521042, | |
| "eval_acc": 0.9327064778624976, | |
| "eval_mcc": 0.8654395363812596, | |
| "eval_f1": 0.9329085217835698, | |
| "eval_auc": 0.978424451662564, | |
| "eval_runtime": 230.3152, | |
| "eval_samples_per_second": 542.109, | |
| "eval_steps_per_second": 8.471, | |
| "epoch": 5.0 | |
| }, | |
| "2": { | |
| "eval_loss": 0.1366845816373825, | |
| "eval_precision": 0.9606142267153541, | |
| "eval_recall": 0.9658351236091961, | |
| "eval_acc": 0.963149253133635, | |
| "eval_mcc": 0.9263124527374313, | |
| "eval_f1": 0.9632176005500173, | |
| "eval_auc": 0.9917985101138621, | |
| "eval_runtime": 234.0218, | |
| "eval_samples_per_second": 533.519, | |
| "eval_steps_per_second": 8.337, | |
| "epoch": 5.0 | |
| }, | |
| "3": { | |
| "eval_loss": 0.05362533777952194, | |
| "eval_precision": 0.984714516412708, | |
| "eval_recall": 0.987382563247507, | |
| "eval_acc": 0.9860398061751632, | |
| "eval_mcc": 0.9720832186237331, | |
| "eval_f1": 0.9860467350320612, | |
| "eval_auc": 0.99819516351748, | |
| "eval_runtime": 301.9859, | |
| "eval_samples_per_second": 413.446, | |
| "eval_steps_per_second": 6.461, | |
| "epoch": 5.0 | |
| }, | |
| "4": { | |
| "eval_loss": 0.030851971358060837, | |
| "eval_precision": 0.9910204081632653, | |
| "eval_recall": 0.9926251322666496, | |
| "eval_acc": 0.991822514116375, | |
| "eval_mcc": 0.9836463262560167, | |
| "eval_f1": 0.9918221211223158, | |
| "eval_auc": 0.9993313443607119, | |
| "eval_runtime": 342.4822, | |
| "eval_samples_per_second": 364.559, | |
| "eval_steps_per_second": 5.697, | |
| "epoch": 5.0 | |
| } | |
| } |