copy bettensor soccer model over to org

Podos_v1_model.py

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+import torch
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+class PodosTransformer(nn.Module,PyTorchModelHubMixin):
+    def __init__(self, input_dim, model_dim, num_classes, num_heads=4, num_layers=2, dropout=0.1,temperature=1):
+        super(PodosTransformer, self).__init__()
+        self.temperature = temperature
+
+        self.projection = nn.Linear(input_dim, model_dim)
+        encoder_layer = nn.TransformerEncoderLayer(d_model=model_dim, nhead=num_heads, dropout=dropout)
+        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.fc = nn.Linear(model_dim, num_classes)
+
+    def forward(self, x):
+        x = self.projection(x)
+        x = x.unsqueeze(1)  
+        x = self.transformer_encoder(x)
+        x = x.mean(dim=1) 
+        x = self.fc(x)
+
+        if self.temperature != 1.0:
+            x = x / self.temperature
+            
+        return x

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Podos v1 Baseline
+
+Podos is a small baseline transformer model for soccer match prediction.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Bettensor | Nickel5
+- **Model type:** PyTorch Transformer
+- **Parameters** 276K parameters
+
+## Uses
+
+Podos predicts soccer match outcomes based on 23 input parameters including sportsbook odds, recent team performance, win/loss streak, and more.
+### Direct Use
+
+For direct use, download the source pytorch class, label_encoder (optional), and load the model. <p><code>PodosTransformer.from_pretrained("Bettensor/podos_soccer_model")</code></p>
+The label encoder contains the id mappings to all teams the model was trained on. 
+Ensure you have Torch installed with:
+<p><code>pip install torch</code></p>
+scikit-learn version 1.4.2 if you want to use the label_encoder:
+<p><code>pip install scikit-learn==1.4.2</code></p>
+newer versions of sklearn may work but are untested.
+
+You also need HuggingFace_hub and safetensors, install with:
+<p><code>pip install huggingface_hub</code></p>
+<p><code>pip install safetensors</code></p>
+  
+model expects 23 parameters for input, with team names mapped as ids:
+- HS - Home shots
+- AS - Away shots
+- HST - Home shots on target
+- AST - Away shots on target
+- HC - Home corners
+- AC - Away corners
+- HO - Home offsides
+- AO - Away offsides
+- HY - Home yellow card
+- AY - Away yellow cards
+- HR - Home red cards
+- AR - Away red cards
+- oddsH - Home win odds
+- oddsD - Draw odds
+- oddsA - Away win odds
+- home_encoded - Home team id
+- away_encoded - Away team id
+- WinStreakHome - Home win streak
+- LossStreakHome - home loss streak
+- WinStreakAway - Away win streak
+- LossStreakAway - Away loss streak
+- HomeTeamForm - Home team recent performance 
+- AwayTeamForm - Away team recent performance
+
+The label_encoder currently contains mappings for 569 unique teams
+
+### Downstream Use
+
+Model is available to use with Bettensor at https://github.com/Bettensor/bettensor
+
+## Bias, Risks, and Limitations
+
+podos v1 presents some home team bias, and may provide overconfident scores to its predicted outcome.
+
+### Recommendations/Future work
+- reduce bias by encoding home field advantage
+- more teams and leagues, especially with more rigorous performance metrics
+- Additional layers for larger input size
+- team embedding layers
+- individual player performance
+
+### Training Data
+Model was trained on 100,000 games with 569 individual teams.
+
+- data source: https://www.football-data.co.uk/downloadm.php
+
+## Model Card Authors
+
+qucat | Nickel5
+
+## Model Card Contact
+www.nickel5.com

config.json

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+{
+  "dropout": 0.1,
+  "input_dim": 23,
+  "model_dim": 32,
+  "num_classes": 3,
+  "num_heads": 4,
+  "num_layers": 2,
+  "temperature": 2
+}

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42e212c9b06d38fc01c25d52a2277f2d65168b96a83f7e123acf1b56913d7eb5
+size 6862

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3eae2e96ac1e85de70214c2ff67e547b408a5f54274f53fc60e44ca04d534905
+size 1106428