Text Classification
Transformers
Safetensors
bert
mentalbert
mental-health
stat3799
text-embeddings-inference
Instructions to use lkh125/stat3799-finetuned-mentalbert-denser-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lkh125/stat3799-finetuned-mentalbert-denser-sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lkh125/stat3799-finetuned-mentalbert-denser-sample")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lkh125/stat3799-finetuned-mentalbert-denser-sample") model = AutoModelForSequenceClassification.from_pretrained("lkh125/stat3799-finetuned-mentalbert-denser-sample") - Notebooks
- Google Colab
- Kaggle
File size: 1,187 Bytes
bcf51e5 | 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 | {
"model_name": "finetuned_mentalbert",
"model_group": "Fine-tuned MentalBERT",
"summary_row": {
"model_group": "Fine-tuned MentalBERT",
"feature_type": "Raw text",
"model_family": "Transformer",
"train_time_sec": 4886.415,
"selection_metric": "micro_f1",
"allow_empty_predictions": true,
"cv_micro_f1": 0.9928926910190938,
"cv_macro_f1": 0.9910607418405284,
"cv_weighted_f1": 0.9928972406388662,
"cv_subset_accuracy": 0.9628528199509574,
"cv_hamming_loss": 0.0030399471313542373,
"test_micro_f1": 0.9924910694758329,
"test_macro_f1": 0.993168188464409,
"test_weighted_f1": 0.9925096900289148,
"test_subset_accuracy": 0.9633802816901409,
"test_hamming_loss": 0.0032237871674491393,
"artifact_path": "/content/drive/MyDrive/STAT3799/Code/artifacts_extended_negative_denser_dataset/checkpoints_train_test_cv/finetuned_mentalbert",
"loaded_from_checkpoint": false
},
"cv_metrics": {
"micro_f1": 0.9928926910190938,
"macro_f1": 0.9910607418405284,
"weighted_f1": 0.9928972406388662,
"subset_accuracy": 0.9628528199509574,
"hamming_loss": 0.0030399471313542373
},
"selected_epochs": 5
} |