Text Classification
Transformers
Safetensors
English
German
xlm-roberta
mental-health
knowledge-graph
text-embeddings-inference
Instructions to use Niklas1102/mentalkg-xlmr-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Niklas1102/mentalkg-xlmr-edge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Niklas1102/mentalkg-xlmr-edge")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Niklas1102/mentalkg-xlmr-edge") model = AutoModelForSequenceClassification.from_pretrained("Niklas1102/mentalkg-xlmr-edge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
upload relation_map.json
Browse files- relation_map.json +78 -0
relation_map.json
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| 1 |
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{
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| 2 |
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"n_source_records": 47714,
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"n_type_pairs": 35,
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| 4 |
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"relation": {
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| 5 |
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"event|emotion": "causes",
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| 6 |
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"thought|emotion": "increases",
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| 7 |
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"event|thought": "causes",
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"stressor|emotion": "causes",
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"stressor|thought": "causes",
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| 10 |
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"emotion|symptom": "increases",
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| 11 |
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"coping_action|symptom": "decreases",
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| 12 |
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"stressor|symptom": "causes",
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| 13 |
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"coping_action|emotion": "causes",
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| 14 |
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"coping_action|thought": "causes",
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| 15 |
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"event|symptom": "causes",
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| 16 |
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"event|coping_action": "linked_to",
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| 17 |
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"emotion|activity": "linked_to",
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| 18 |
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"symptom|activity": "linked_to",
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"symptom|emotion": "follows",
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| 20 |
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"event|activity": "linked_to",
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"emotion|thought": "decreases",
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| 22 |
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"thought|activity": "linked_to",
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| 23 |
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"stressor|activity": "linked_to",
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| 24 |
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"activity|emotion": "causes",
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| 25 |
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"thought|symptom": "increases",
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| 26 |
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"activity|symptom": "decreases",
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| 27 |
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"activity|coping_action": "linked_to",
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| 28 |
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"emotion|emotion": "decreases",
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| 29 |
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"symptom|symptom": "follows",
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| 30 |
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"symptom|thought": "follows",
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| 31 |
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"event|stressor": "linked_to",
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| 32 |
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"stressor|coping_action": "linked_to",
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| 33 |
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"activity|thought": "linked_to",
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| 34 |
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"event|event": "follows",
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| 35 |
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"stressor|stressor": "linked_to",
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| 36 |
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"thought|thought": "increases",
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| 37 |
+
"coping_action|activity": "decreases",
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| 38 |
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"coping_action|coping_action": "decreases",
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| 39 |
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"activity|activity": "linked_to"
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| 40 |
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},
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| 41 |
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"count": {
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| 42 |
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"event|emotion": 30723,
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| 43 |
+
"thought|emotion": 32245,
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| 44 |
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"event|thought": 11462,
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| 45 |
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"stressor|emotion": 29393,
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| 46 |
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"stressor|thought": 10693,
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| 47 |
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"emotion|symptom": 16319,
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| 48 |
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"coping_action|symptom": 13463,
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| 49 |
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"stressor|symptom": 6095,
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| 50 |
+
"coping_action|emotion": 14081,
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| 51 |
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"coping_action|thought": 2637,
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| 52 |
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"event|symptom": 10449,
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| 53 |
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"event|coping_action": 2677,
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| 54 |
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"emotion|activity": 12712,
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| 55 |
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"symptom|activity": 2663,
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| 56 |
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"symptom|emotion": 5173,
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| 57 |
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"event|activity": 2985,
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| 58 |
+
"emotion|thought": 3644,
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| 59 |
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"thought|activity": 1934,
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| 60 |
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"stressor|activity": 2230,
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| 61 |
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"activity|emotion": 5886,
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| 62 |
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"thought|symptom": 6216,
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| 63 |
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"activity|symptom": 2319,
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| 64 |
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"activity|coping_action": 151,
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| 65 |
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"emotion|emotion": 7205,
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| 66 |
+
"symptom|symptom": 3248,
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| 67 |
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"symptom|thought": 2819,
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| 68 |
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"event|stressor": 3537,
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| 69 |
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"stressor|coping_action": 4323,
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| 70 |
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"activity|thought": 2480,
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| 71 |
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"event|event": 1502,
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| 72 |
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"stressor|stressor": 838,
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| 73 |
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"thought|thought": 221,
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| 74 |
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"coping_action|activity": 75,
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| 75 |
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"coping_action|coping_action": 79,
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| 76 |
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"activity|activity": 139
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| 77 |
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}
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| 78 |
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}
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