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
| { | |
| "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 | |
| } |