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README.md

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+---
+license: mit
+tags:
+- pytorch
+- framenet
+- multimodal
+- neuro-symbolic
+library_name: pytorch
+---
+
+# 🧠 ReINVenTA: Event Classifier (Fusion WiSE)
+
+Este é o modelo **SOTA** do Estágio 3. Classifica **Eventos Semânticos** combinando visão (CLIP) e lógica (YOLO).
+
+## 📊 Performance
+- **Recall@5:** 68.1%
+- **Arquitetura:** Fusion WiSE (Late Fusion MLP)
+
+## 💻 Como usar (Python API)
+
+### 1. Instalação
+```bash
+pip install huggingface_hub torch
+```
+
+### 2. Código de Inferência
+```python
+import torch
+import json
+from huggingface_hub import hf_hub_download
+import sys
+import os
+
+# 1. Baixar arquivos
+repo_id = "FrameNetBrasil/reinventa-event-classifier-fusion-wise"
+model_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.pth")
+code_path = hf_hub_download(repo_id=repo_id, filename="model.py")
+ent_path = hf_hub_download(repo_id=repo_id, filename="entities.json")
+evt_path = hf_hub_download(repo_id=repo_id, filename="events.json")
+
+# 2. Importar Classe Customizada
+sys.path.append(os.path.dirname(code_path))
+from model import FusionWiSE
+
+# 3. Carregar
+with open(ent_path) as f: ents = json.load(f)
+with open(evt_path) as f: evts = json.load(f)
+
+model = FusionWiSE(clip_dim=512, entity_dim=len(ents), num_classes=len(evts))
+model.load_state_dict(torch.load(model_path, map_location='cpu'))
+model.eval()
+print('✅ Modelo carregado!')
+```

entities.json

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+[
+  "Accoutrements.Accoutrement",
+  "Accoutrements.Descriptor",
+  "Accoutrements.Wearer",
+  "Activity.Activity",
+  "Aggregate.Aggregate",
+  "Aggregate.Individuals",
+  "Animals.Animal",
+  "Animals.Characteristic",
+  "Appliances.Appliance",
+  "Architectural_part.Part",
+  "Architectural_part.Whole",
+  "Artifact.Artifact",
+  "Artifact.Material",
+  "Artifact.Name",
+  "Artifact.Place",
+  "Artifact.Use",
+  "Artifact_subpart.Artifact",
+  "Assemble.Individuals",
+  "Athletes.Athlete",
+  "Athletes_by_position.Position",
+  "Athletes_by_sport.Athlete",
+  "Basis_for_attribute.Figure",
+  "Biological_area.Locale",
+  "Body_decoration.Decoration",
+  "Body_parts.Body_part",
+  "Body_parts.Possessor",
+  "Building_subparts.Building_part",
+  "Building_subparts.Place",
+  "Building_subparts.Whole",
+  "Buildings.Building",
+  "Businesses.Business",
+  "Businesses.Place",
+  "Calendric_unit.Relative_time",
+  "Calendric_unit.Unit",
+  "Cardinal_numbers.Number",
+  "Clothing.Descriptor",
+  "Clothing.Garment",
+  "Clothing.Style",
+  "Clothing.Wearer",
+  "Clothing_parts.Clothing",
+  "Clothing_parts.Subpart",
+  "Clothing_parts.Wearer",
+  "Color.Color",
+  "Communication_means.Means",
+  "Connecting_architecture.Part",
+  "Connectors.Connector",
+  "Containers.Container",
+  "Create_representation.Representation",
+  "Dining.Establishment",
+  "Entity.Constituent_parts",
+  "Entity.Context_of_acquaintance",
+  "Entity.Descriptor",
+  "Entity.Discipline",
+  "Entity.Entity",
+  "Entity.Ethnicity",
+  "Entity.Part",
+  "Entity.Type",
+  "Entity.Use",
+  "Event.Event",
+  "Eventive.Place",
+  "Eventive.Theme",
+  "Facial_expression.Expression",
+  "Fire_burning.Fire",
+  "Food.Food",
+  "Furniture.Piece_of_furniture",
+  "Gizmo.Gizmo",
+  "Hair_configuration.Configuration",
+  "Hair_configuration.Hair",
+  "Information_display.Display",
+  "Ingredients.Material",
+  "Intoxicants.Intoxicant",
+  "Kinship.Alter",
+  "Kinship.Ego",
+  "Limitation.Entity",
+  "Locale.Descriptor",
+  "Locale.Locale",
+  "Locale.Name",
+  "Locale.Relative_location",
+  "Locale_by_event.Locale",
+  "Locale_by_use.Locale",
+  "Locative_relation.Ground",
+  "Machinery.Machinery",
+  "Means_of_transportation.Means_of_transportation",
+  "Moves.Move",
+  "Musical_instruments.Musical_instrument",
+  "Natural_features.Constituent_parts",
+  "Natural_features.Locale",
+  "Part_piece.Piece",
+  "Part_whole.Part",
+  "People.Age",
+  "People.Descriptor",
+  "People.Persistent_characteristic",
+  "People.Person",
+  "People_by_age.Age",
+  "People_by_age.Persistent_characteristic",
+  "People_by_age.Person",
+  "People_by_ethnicity.Person",
+  "People_by_leisure_activity.Leisure_activity",
+  "People_by_leisure_activity.Person",
+  "People_by_origin.Person",
+  "People_by_social_framing.Person",
+  "People_by_transitory_activity.Person",
+  "People_by_vocation.Context_of_acquaintance",
+  "People_by_vocation.Person",
+  "People_by_vocation.Type",
+  "Performers_and_roles.Place",
+  "Performing_arts.Performance",
+  "Performing_arts.Performer",
+  "Personal_relationship.Partner_1",
+  "Personal_relationship.Partner_2",
+  "Personal_relationship.Partners",
+  "Physical_artworks.Artifact",
+  "Plants.Plant",
+  "Political_locales.Locale",
+  "Precipitation.Precipitation",
+  "Representing.Entity",
+  "Roadways.Roadway",
+  "Setting_fire.Flame",
+  "Shaped_part.Part",
+  "Shapes.Shape",
+  "Social_event.Social_event",
+  "Sports.Sport",
+  "Sports_equipment.Equipment",
+  "Sports_event.Event",
+  "Sports_event.Sport",
+  "Sports_venues.Place",
+  "Sports_venues.Venue",
+  "Sports_venues_subparts.Part",
+  "Sports_venues_subparts.Venue",
+  "Substance.Substance",
+  "Substance_by_phase.Substance",
+  "Text.Medium",
+  "Text.Text",
+  "Toys.Toy",
+  "Transportation.Transit",
+  "Urban_furniture.Furniture",
+  "Utensils.Utensil",
+  "Vehicle.Vehicle",
+  "Vehicle_subpart.Part",
+  "Weapon.Weapon",
+  "Weather.Place",
+  "Weather.Time",
+  "frm_ferramenta.fe_Ferramenta",
+  "frm_patterns.fe_Patterns_Shape",
+  "frm_people_by_transit_mode.frm_people_by_transit_mode_fe_person_2618_tpl_people",
+  "frm_pessoas_por_g\u00eanero.fe_pessoas_por_g\u00eanero_g\u00eanero",
+  "frm_pessoas_por_g\u00eanero.fe_pessoas_por_g\u00eanero_pessoas"
+]

events.json

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+[
+  "Abounding_with",
+  "Activity",
+  "Activity_ongoing",
+  "Activity_prepare",
+  "Adjacency",
+  "Agriculture",
+  "Arranging",
+  "Arriving",
+  "Artifact",
+  "Assistance",
+  "Athletes",
+  "Attaching",
+  "Attempt",
+  "Attributes",
+  "Being_located",
+  "Body_movement",
+  "Bringing",
+  "Building",
+  "Cause_fluidic_motion",
+  "Cause_harm",
+  "Cause_impact",
+  "Cause_motion",
+  "Cause_to_make_noise",
+  "Cause_to_perceive",
+  "Change_posture",
+  "Chatting",
+  "Closure",
+  "Clothing",
+  "Come_together",
+  "Commerce_buy",
+  "Commerce_sell",
+  "Communication",
+  "Competition",
+  "Cooking_creation",
+  "Cotheme",
+  "Create_physical_artwork",
+  "Create_representation",
+  "Crowd_reactions",
+  "Cutting",
+  "Departing",
+  "Distributed_position",
+  "Do_phisical_activity",
+  "Emotions",
+  "Emptying",
+  "Entity",
+  "Event",
+  "Eventive",
+  "Exercising",
+  "Experiencer_focused_emotion",
+  "Facial_expression",
+  "Filling",
+  "Fluidic_motion",
+  "Gesture",
+  "Give_impression",
+  "Giving",
+  "Go_dancing",
+  "Gradable_attributes",
+  "Grooming",
+  "Hostile_encounter",
+  "Hunting",
+  "Individual_moves",
+  "Ingest_substance",
+  "Ingestion",
+  "Inspecting",
+  "Intentional_traversing",
+  "Intentionally_act",
+  "Intentionally_affect",
+  "Intentionally_create",
+  "Interactive_moves",
+  "Locative_relation",
+  "Make_noise",
+  "Making_faces",
+  "Manipulation",
+  "Manufacturing",
+  "Motion",
+  "Motion_directional",
+  "Moves",
+  "Moving_in_place",
+  "Musical_instruments",
+  "Operate_vehicle",
+  "Operating_a_system",
+  "Participation",
+  "People",
+  "Perception_active",
+  "Performing_arts",
+  "Placing",
+  "Political_actions",
+  "Posture",
+  "Practice",
+  "Reading_activity",
+  "Reading_perception",
+  "Reciprocality",
+  "Removing",
+  "Ride_vehicle",
+  "Scenario",
+  "Scrutiny",
+  "Self_motion",
+  "Sleep",
+  "Social_event",
+  "Speak_on_topic",
+  "Sports_event",
+  "State_continue",
+  "Statement",
+  "Subjective_experience",
+  "Surrounding",
+  "Taking",
+  "Telling",
+  "Text_creation",
+  "Tourist_activities",
+  "Transitive_action",
+  "Traversing",
+  "Using",
+  "Waiting",
+  "Wearing",
+  "Work"
+]

model.py

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+
+import torch
+import torch.nn as nn
+
+class FusionWiSE(nn.Module):
+    def __init__(self, clip_dim=512, entity_dim=147, num_classes=115, hidden_dim=512, dropout=0.5):
+        super(FusionWiSE, self).__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(clip_dim + entity_dim, hidden_dim),
+            nn.BatchNorm1d(hidden_dim),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, hidden_dim) 
+        )
+        self.classifier = nn.Linear(hidden_dim, num_classes)
+
+    def forward(self, clip_emb, entity_onehot):
+        x = torch.cat([clip_emb, entity_onehot], dim=1)
+        feats = self.encoder(x)
+        feats = feats / feats.norm(p=2, dim=-1, keepdim=True)
+        return self.classifier(feats)

pytorch_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e33fafe67d44ddaeb7813fde45784c2c635a2c300994df2c3aa51569399f11c6
+size 2650797