Text Classification
Transformers
PyTorch
English
distilbert
abuse-detection
relationship-analysis
multi-label-classification
safety
psychology
nlp
Eval Results (legacy)
text-embeddings-inference
Instructions to use SamanthaStorm/abusedetector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamanthaStorm/abusedetector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SamanthaStorm/abusedetector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AbusePatternDetector tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/abusedetector") model = AbusePatternDetector.from_pretrained("SamanthaStorm/abusedetector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from SamanthaStorm/abusedetector: direct link, hf CLI and curl.
- Browser
- Download file 2.63 kB
-
https://huggingface.co/SamanthaStorm/abusedetector/resolve/main/config.json
- Command line
-
hf download hf://SamanthaStorm/abusedetector/config.json
-
curl -L -o config.json https://huggingface.co/SamanthaStorm/abusedetector/resolve/main/config.json
2.63 kB
| { | |
| "architectures": [ | |
| "AbusePatternDetector" | |
| ], | |
| "model_type": "distilbert", | |
| "num_labels": 8, | |
| "id2label": { | |
| "0": "surveillance_accusation", | |
| "1": "darvo", | |
| "2": "gaslighting", | |
| "3": "boundary_violation", | |
| "4": "emotional_manipulation", | |
| "5": "victim_blaming", | |
| "6": "healthy_boundary_setting", | |
| "7": "no_abuse_pattern" | |
| }, | |
| "label2id": { | |
| "surveillance_accusation": 0, | |
| "darvo": 1, | |
| "gaslighting": 2, | |
| "boundary_violation": 3, | |
| "emotional_manipulation": 4, | |
| "victim_blaming": 5, | |
| "healthy_boundary_setting": 6, | |
| "no_abuse_pattern": 7 | |
| }, | |
| "base_model": "distilbert-base-uncased", | |
| "task_specific_params": { | |
| "text-classification": { | |
| "problem_type": "multi_label_classification", | |
| "num_labels": 8 | |
| } | |
| }, | |
| "label_columns": [ | |
| "surveillance_accusation", | |
| "darvo", | |
| "gaslighting", | |
| "boundary_violation", | |
| "emotional_manipulation", | |
| "victim_blaming", | |
| "healthy_boundary_setting", | |
| "no_abuse_pattern" | |
| ], | |
| "optimal_threshold": 0.05, | |
| "model_architecture": "AbusePatternDetector", | |
| "training_metrics": { | |
| "test_f1_macro": 0.27733516483516485, | |
| "test_f1_micro": 0.2857142857142857, | |
| "hamming_loss": 0.8333333333333334, | |
| "jaccard_score": 0.16666666666666666, | |
| "optimal_threshold": 0.05, | |
| "per_label_f1": { | |
| "surveillance_accusation": { | |
| "precision": 0.16666666666666666, | |
| "recall": 1.0, | |
| "f1": 0.2857142857142857, | |
| "support": 2 | |
| }, | |
| "darvo": { | |
| "precision": 0.08333333333333333, | |
| "recall": 1.0, | |
| "f1": 0.15384615384615385, | |
| "support": 1 | |
| }, | |
| "gaslighting": { | |
| "precision": 0.16666666666666666, | |
| "recall": 1.0, | |
| "f1": 0.2857142857142857, | |
| "support": 2 | |
| }, | |
| "boundary_violation": { | |
| "precision": 0.08333333333333333, | |
| "recall": 1.0, | |
| "f1": 0.15384615384615385, | |
| "support": 1 | |
| }, | |
| "emotional_manipulation": { | |
| "precision": 0.25, | |
| "recall": 1.0, | |
| "f1": 0.4, | |
| "support": 3 | |
| }, | |
| "victim_blaming": { | |
| "precision": 0.08333333333333333, | |
| "recall": 1.0, | |
| "f1": 0.15384615384615385, | |
| "support": 1 | |
| }, | |
| "healthy_boundary_setting": { | |
| "precision": 0.16666666666666666, | |
| "recall": 1.0, | |
| "f1": 0.2857142857142857, | |
| "support": 2 | |
| }, | |
| "no_abuse_pattern": { | |
| "precision": 0.3333333333333333, | |
| "recall": 1.0, | |
| "f1": 0.5, | |
| "support": 4 | |
| } | |
| }, | |
| "precision_macro": 0.16666666666666666, | |
| "recall_macro": 1.0 | |
| } | |
| } |