Update README.md

README.md

@@ -5,197 +5,113 @@ language:
 pipeline_tag: zero-shot-classification
 ---
 
-# Model Card for Model ID
+# Tiny-Toxic-Detector
+
+A tiny comment toxicity classifier model at only 2M parameters. With only ~10MB ram and fast inference we bring you one of the best toxicity classifiers that outperforms models over 50 times its size.
+
+A paper on this model is being released soon.
+
+
+### Usage
+This model uses custom architecture and requires some extra custom code to work. Below you can find a script that is fully-usable.
+```python
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig, AutoTokenizer
+from huggingface_hub import login
+import os
+
+# Define TinyTransformer model
+class TinyTransformer(nn.Module):
+    def __init__(self, vocab_size, embed_dim, num_heads, ff_dim, num_layers):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim)
+        self.pos_encoding = nn.Parameter(torch.zeros(1, 512, embed_dim))
+        encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dim_feedforward=ff_dim, batch_first=True)
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.fc = nn.Linear(embed_dim, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.embedding(x) + self.pos_encoding[:, :x.size(1), :]
+        x = self.transformer(x)
+        x = x.mean(dim=1)  # Global average pooling
+        x = self.fc(x)
+        return self.sigmoid(x)
+
+class TinyTransformerConfig(PretrainedConfig):
+    model_type = "tiny_transformer"
+
+    def __init__(self, vocab_size=30522, embed_dim=64, num_heads=2, ff_dim=128, num_layers=4, max_position_embeddings=512, **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.ff_dim = ff_dim
+        self.num_layers = num_layers
+        self.max_position_embeddings = max_position_embeddings
+
+class TinyTransformerForSequenceClassification(PreTrainedModel):
+    config_class = TinyTransformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = 1
+        self.transformer = TinyTransformer(
+            config.vocab_size,
+            config.embed_dim,
+            config.num_heads,
+            config.ff_dim,
+            config.num_layers
+        )
+
+    def forward(self, input_ids, attention_mask=None):
+        outputs = self.transformer(input_ids)
+        return {"logits": outputs}
+
+# Load the Tiny-Toxic-Detector model and tokenizer
+def load_model_and_tokenizer():
+    device = torch.device("cpu") # Due to GPU overhead inference is faster on CPU!
+
+    # Load Tiny-toxic-detector
+    config = TinyTransformerConfig.from_pretrained("AssistantsLab/Tiny-Toxic-Detector")
+    model = TinyTransformerForSequenceClassification.from_pretrained("AssistantsLab/Tiny-Toxic-Detector", config=config).to(device)
+    tokenizer = AutoTokenizer.from_pretrained("AssistantsLab/Tiny-Toxic-Detector")
+
+    return model, tokenizer, device
+
+# Prediction function
+def predict_toxicity(text, model, tokenizer, device):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128, padding="max_length").to(device)
+    if "token_type_ids" in inputs:
+        del inputs["token_type_ids"]
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs["logits"].squeeze()
+    prediction = "Toxic" if logits > 0.5 else "Not Toxic"
+    return prediction
+
+def main():
+    model, tokenizer, device = load_model_and_tokenizer()
+
+    while True:
+        print("Enter text to classify (or type 'exit' to quit):")
+        text = input()
+
+        if text.lower() == 'exit':
+            print("Exiting...")
+            break
+
+        if text:
+            prediction = predict_toxicity(text, model, tokenizer, device)
+            print(f"Prediction: {prediction}")
+        else:
+            print("No text provided. Please enter some text.")
+
+if __name__ == "__main__":
+    main()
+```
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
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-
-
-#### Training Hyperparameters
-
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-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
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-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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