Text Classification
Transformers
Safetensors
English
roberta
ag-news
salient-keywords
experiment
text-embeddings-inference
Instructions to use martian786/agnews-salient-random-k16-seed-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use martian786/agnews-salient-random-k16-seed-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="martian786/agnews-salient-random-k16-seed-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("martian786/agnews-salient-random-k16-seed-1") model = AutoModelForSequenceClassification.from_pretrained("martian786/agnews-salient-random-k16-seed-1") - Notebooks
- Google Colab
- Kaggle
File size: 1,973 Bytes
d39e1c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"run_name": "random_k16",
"variant": "random",
"hf_repo_id": "martian786/agnews-salient-random-k16-seed-1",
"fraction": 0.25,
"train_examples": 28500,
"val_examples": 6000,
"test_examples": 7600,
"val_eval_loss": 0.38659048080444336,
"val_eval_accuracy": 0.8825,
"val_eval_macro_f1": 0.8822514612482859,
"test_accuracy": 0.8698684210526316,
"test_precision_macro": 0.8697436317086674,
"test_recall_macro": 0.8698684210526315,
"test_f1_macro": 0.8696299974829994,
"accuracy_ci_lower": 0.862116882759214,
"accuracy_ci_upper": 0.8772462307490949,
"num_misclassified": 989,
"accuracy_per_1k_train_examples": 0.030521698984302865,
"representation_preprocess_time_s": 3.5573337269997864,
"tokenization_time_s": 5.769618768000328,
"classifier_train_time_s": 655.2569652520006,
"batched_predict_total_s": 15.153196565999679,
"inference_latency_mean_ms": 8.140939455010994,
"inference_latency_median_ms": 7.902555000327993,
"inference_latency_p95_ms": 9.573150999995047,
"inference_throughput_sps": 122.83594608782772,
"batched_predict_latency_mean_ms": 1.9938416534210106,
"trainer_reported_train_runtime_s": 654.6613,
"learning_curve_outputs": {
"trainer_log_history_csv": "agnews_salient_runs_second_seed_1/random_k16/trainer_log_history.csv",
"loss_curve_png": "agnews_salient_runs_second_seed_1/random_k16/loss_curve.png",
"validation_accuracy_curve_png": "agnews_salient_runs_second_seed_1/random_k16/validation_accuracy_curve.png",
"validation_macro_f1_curve_png": "agnews_salient_runs_second_seed_1/random_k16/validation_macro_f1_curve.png"
},
"test_plot_outputs": {
"test_macro_metrics_bar_png": "agnews_salient_runs_second_seed_1/random_k16/test_macro_metrics_bar.png",
"test_per_class_f1_bar_png": "agnews_salient_runs_second_seed_1/random_k16/test_per_class_f1_bar.png",
"test_confusion_matrix_png": "agnews_salient_runs_second_seed_1/random_k16/test_confusion_matrix.png"
}
} |