Graph Machine Learning
Transformers
Safetensors
English
unicosys_hypergraph
knowledge-graph
hypergraph
legal-evidence
graph-neural-network
unicosys
Instructions to use drzo/unicosys-hypergraph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drzo/unicosys-hypergraph with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("drzo/unicosys-hypergraph", dtype="auto") - Notebooks
- Google Colab
- Kaggle
chore: update unicosys hypergraph model from CI pipeline
Browse files- README.md +6 -6
- config.json +3 -3
- graph_data.safetensors +2 -2
- model.safetensors +2 -2
- model_summary.json +7 -7
- node_id_mapping.json +0 -0
- tokenizer.json +30 -38
README.md
CHANGED
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@@ -32,17 +32,17 @@ single trainable knowledge representation.
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| Text Encoder | 2-layer Transformer, 4 heads |
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| Graph Attention | 2-layer GAT, 4 heads |
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| Link Predictor | 2-layer MLP with margin ranking loss |
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-
| Total Parameters | **34,
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### Knowledge Graph Statistics
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| Metric | Count |
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|---|---|
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| Total Nodes | 198,
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| Total Edges | 13,
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| Cross-Links | 3,
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| Entities | 16 |
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-
| Emails | 197,
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| Financial Documents | 0 |
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| Timeline Events | 10 |
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| LEX Schemes | 0 |
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@@ -54,7 +54,7 @@ single trainable knowledge representation.
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|---|---|
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| Core (Entities) | 16 |
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| Fincosys (Financial) | 0 |
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| Comcosys (Communications) | 197,
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| RevStream1 (Evidence) | 0 |
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| Ad-Res-J7 (Legal) | 10 |
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| Text Encoder | 2-layer Transformer, 4 heads |
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| Graph Attention | 2-layer GAT, 4 heads |
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| Link Predictor | 2-layer MLP with margin ranking loss |
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+
| Total Parameters | **34,762,753** |
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### Knowledge Graph Statistics
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| Metric | Count |
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|---|---|
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| Total Nodes | 198,019 |
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| Total Edges | 13,419 |
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| Cross-Links | 3,664 |
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| Entities | 16 |
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| 45 |
+
| Emails | 197,993 |
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| 46 |
| Financial Documents | 0 |
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| 47 |
| Timeline Events | 10 |
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| 48 |
| LEX Schemes | 0 |
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| 54 |
|---|---|
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| 55 |
| Core (Entities) | 16 |
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| 56 |
| Fincosys (Financial) | 0 |
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| 57 |
+
| Comcosys (Communications) | 197,993 |
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| 58 |
| RevStream1 (Evidence) | 0 |
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| Ad-Res-J7 (Legal) | 10 |
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| 60 |
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config.json
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@@ -26,10 +26,10 @@
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"entity": 3,
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"timeline_event": 4
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},
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"num_cross_links":
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"num_edge_types": 4,
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"num_entities": 16,
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"num_evidence":
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"num_node_types": 5,
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"num_subsystems": 5,
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"subsystem_vocab": {
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"text_max_length": 128,
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"text_num_heads": 4,
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"text_num_layers": 2,
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"text_vocab_size":
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"transformers_version": "5.13.0"
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}
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"entity": 3,
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"timeline_event": 4
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},
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"num_cross_links": 3664,
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"num_edge_types": 4,
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"num_entities": 16,
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"num_evidence": 198019,
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"num_node_types": 5,
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"num_subsystems": 5,
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"subsystem_vocab": {
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"text_max_length": 128,
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"text_num_heads": 4,
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"text_num_layers": 2,
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"text_vocab_size": 238,
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"transformers_version": "5.13.0"
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}
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graph_data.safetensors
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size 5096976
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model.safetensors
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size 139057860
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model_summary.json
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{
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"case_number": "2025-137857",
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"total_nodes":
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"total_cross_links":
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"node_types": {
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"entity": 16,
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"email":
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"timeline_event": 10
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"edge_types": {
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"proves":
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"transaction_evidenced_by": 1
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"subsystems": {
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"core": 16,
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"comcosys":
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"ad_res_j7": 10
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},
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"model_params":
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"model_architecture": {
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"node_embed_dim": 128,
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"text_embed_dim": 256,
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{
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"case_number": "2025-137857",
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"total_nodes": 198019,
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"total_edges": 14338,
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"total_cross_links": 3664,
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"node_types": {
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"email": 197993,
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"timeline_event": 10
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"transaction_evidenced_by": 1
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"subsystems": {
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"core": 16,
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"comcosys": 197993,
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"ad_res_j7": 10
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"model_params": 34762753,
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"model_architecture": {
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"node_embed_dim": 128,
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"text_embed_dim": 256,
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node_id_mapping.json
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tokenizer.json
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