Delete lionguard2.py

lionguard2.py

@@ -1,170 +0,0 @@
-"""
-lionguard2.py
-"""
-
-import torch
-import torch.nn as nn
-
-CATEGORIES = {
-    "binary": ["binary"],
-    "hateful": ["hateful_l1", "hateful_l2"],
-    "insults": ["insults"],
-    "sexual": [
-        "sexual_l1",
-        "sexual_l2",
-    ],
-    "physical_violence": ["physical_violence"],
-    "self_harm": ["self_harm_l1", "self_harm_l2"],
-    "all_other_misconduct": [
-        "all_other_misconduct_l1",
-        "all_other_misconduct_l2",
-    ],
-}
-
-INPUT_DIMENSION = 3072  # length of OpenAI embeddings
-
-
-class LionGuard2(nn.Module):
-    def __init__(
-        self,
-        input_dim=INPUT_DIMENSION,
-        label_names=CATEGORIES.keys(),
-        categories=CATEGORIES,
-    ):
-        """
-        LionGuard2 is a localised content moderation model that flags whether text violates the following categories:
-
-        1. `hateful`: Text that discriminates, criticizes, insults, denounces, or dehumanizes a person or group on the basis of a protected identity.
-
-        There are two sub-categories for the `hateful` category:
-        a. `level_1_discriminatory`: Text that contains derogatory or generalized negative statements targeting a protected group.
-        b. `level_2_hate_speech`: Text that explicitly calls for harm or violence against a protected group; or language praising or justifying violence against them.
-
-        2. `insults`: Text that insults demeans, humiliates, mocks, or belittles a person or group **without** referencing a legally protected trait.
-        For example, this includes personal attacks on attributes such as someone’s appearance, intellect, behavior, or other non-protected characteristics.
-
-        3. `sexual`: Text that depicts or indicates sexual interest, activity, or arousal, using direct or indirect references to body parts, sexual acts, or physical traits.
-        This includes sexual content that may be inappropriate for certain audiences.
-
-        There are two sub-categories for the `sexual` category:
-        a. `level_1_not_appropriate_for_minors`: Text that contains mild-to-moderate sexual content that is generally adult-oriented or potentially unsuitable for those under 16.
-            May include matter-of-fact discussions about sex, sexuality, or sexual preferences.
-        b. `level_2_not_appropriate_for_all_ages`: Text that contains content aimed at adults and considered explicit, graphic, or otherwise inappropriate for a broad audience.
-            May include explicit descriptions of sexual acts, detailed sexual fantasies, or highly sexualized content.
-
-        4. `physical_violence`: Text that includes glorification of violence or threats to inflict physical harm or injury on a person, group, or entity.
-
-        5. `self_harm`: Text that promotes, suggests, or expresses intent to self-harm or commit suicide.
-
-        There are two sub-categories for the `self_harm` category:
-        a. `level_1_self_harm_intent`: Text that expresses suicidal thoughts or self-harm intention; or content encouraging someone to self-harm.
-        b. `level_2_self_harm_action`: Text that describes or indicates ongoing or imminent self-harm behavior.
-
-        6. `all_other_misconduct`: This is a catch-all category for any other unsafe text that does not fit into the other categories.
-        It includes text that seeks or provides information about engaging in misconduct, wrongdoing, or criminal activity, or that threatens to harm,
-        defraud, or exploit others. This includes facilitating illegal acts (under Singapore law) or other forms of socially harmful activity.
-
-        There are two sub-categories for the `all_other_misconduct` category:
-        a. `level_1_not_socially_accepted`: Text that advocates or instructs on unethical/immoral activities that may not necessarily be illegal but are socially condemned.
-        b. `level_2_illegal_activities`: Text that seeks or provides instructions to carry out clearly illegal activities or serious wrongdoing; includes credible threats of severe harm.
-
-        Lastly, there is an additional `binary` category (#7) which flags whether the text is unsafe in general.
-
-        The model takes in as input text, after it has been encoded with OpenAI's `text-embedding-3-small` model.
-
-        The model outputs the probabilities of each category being true.
-
-        ================================
-
-        Args:
-            input_dim: The dimension of the input embeddings. This defaults to 3072, which is the dimension of the embeddings from OpenAI's `text-embedding-3-small` model. This should not be changed.
-            label_names: The names of the labels. This defaults to the keys of the CATEGORIES dictionary. This should not be changed.
-            categories: The categories of the labels. This defaults to the CATEGORIES dictionary. This should not be changed.
-
-        Returns:
-            A LionGuard2 model.
-        """
-        super(LionGuard2, self).__init__()
-        self.label_names = label_names
-        self.n_outputs = len(label_names)
-        self.categories = categories
-
-        # Shared layers
-        self.shared_layers = nn.Sequential(
-            nn.Linear(input_dim, 256),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(256, 128),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-        )
-
-        # Output heads for each label
-        self.output_heads = nn.ModuleList(
-            [
-                nn.Sequential(
-                    nn.Linear(128, 32),
-                    nn.ReLU(),
-                    nn.Linear(32, 2),  # 2 thresholds for ordinal classification
-                    nn.Sigmoid(),
-                )
-                for _ in range(self.n_outputs)
-            ]
-        )
-
-    def forward(self, x):
-        # Pass through shared layers
-        h = self.shared_layers(x)
-        # Pass through each output head
-        return [head(h) for head in self.output_heads]
-
-    def predict(self, embeddings):
-        """
-        Predict the probabilities of each label being true.
-
-        Args:
-            embeddings: A numpy array of embeddings (N * INPUT_DIMENSION)
-
-        Returns:
-            A dictionary of probabilities.
-        """
-        # Convert input to PyTorch tensor if not already
-        if not isinstance(embeddings, torch.Tensor):
-            x = torch.tensor(embeddings, dtype=torch.float32)
-        else:
-            x = embeddings
-
-        # Pass through model
-        with torch.no_grad():
-            outputs = self.forward(x)
-
-        # Stack outputs into a single tensor
-        raw_predictions = torch.stack(outputs)  # SIZE:
-
-        # Extract and format probabilities from raw predictions
-        output = {}
-        for i, main_cat in enumerate(self.label_names):
-            sub_categories = self.categories[main_cat]
-            for j, sub_cat in enumerate(sub_categories):
-                # j=0 uses P(y>0)
-                # j=1 uses P(y>1) if L2 category exists
-                output[sub_cat] = raw_predictions[i, :, j]
-
-            # Post processing step:
-            # If L2 category exists, and P(L2) > P(L1),
-            # Set both P(L1) and P(L2) to their average to maintain ordinal consistency
-            if len(sub_categories) > 1:
-                l1 = output[sub_categories[0]]
-                l2 = output[sub_categories[1]]
-
-                # Update probabilities on samples where P(L2) > P(L1)
-                mask = l2 > l1
-                mean_prob = (l1 + l2) / 2
-                l1[mask] = mean_prob[mask]
-                l2[mask] = mean_prob[mask]
-                output[sub_categories[0]] = l1
-                output[sub_categories[1]] = l2
-
-        for key, value in output.items():
-            output[key] = value.numpy().tolist()
-        return output