---
language:
- en
---

# Text Classification Toxicity

This model is a fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large) on the on the [Jigsaw 1st Kaggle competition](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) dataset using [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) as teacher model.
The original unquantized model can be found [here](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-lite).

The model contains two labels only (toxicity and severe toxicity). For the model with all labels refer to this [page](https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw)

# Usage

## Installation

```bash
pip install tokenizers
pip install onnxruntime
git clone https://huggingface.co/minuva/MiniLMv2-toxic-jigsaw-lite-onnx
```
## Load the Model
```py
import os
import numpy as np
import json

from tokenizers import Tokenizer
from onnxruntime import InferenceSession


model_name = "minuva/MiniLMv2-toxic-jigsaw-lite-onnx"
tokenizer = Tokenizer.from_pretrained(model_name)
tokenizer.enable_padding()
tokenizer.enable_truncation(max_length=256)
batch_size = 16

texts = ["This is pure trash",]
outputs = []
model = InferenceSession("MiniLMv2-toxic-jigsaw-lite-onnx/model_optimized_quantized.onnx", providers=['CPUExecutionProvider'])

with open(os.path.join("MiniLMv2-toxic-jigsaw-lite-onnx", "config.json"), "r") as f:
            config = json.load(f)

output_names = [output.name for output in model.get_outputs()]
input_names = [input.name for input in model.get_inputs()]

for subtexts in np.array_split(np.array(texts), len(texts) // batch_size + 1):
            encodings = tokenizer.encode_batch(list(subtexts))
            inputs = {
                "input_ids": np.vstack(
                    [encoding.ids for encoding in encodings],
                ),
                "attention_mask": np.vstack(
                    [encoding.attention_mask for encoding in encodings],
                ),
                "token_type_ids": np.vstack(
                    [encoding.type_ids for encoding in encodings],
                ),
            }

            for input_name in input_names:
                if input_name not in inputs:
                    raise ValueError(f"Input name {input_name} not found in inputs")

            inputs = {input_name: inputs[input_name] for input_name in input_names}
            output = np.squeeze(
                np.stack(
                    model.run(output_names=output_names, input_feed=inputs)
                ),
                axis=0,
            )
            outputs.append(output)

outputs = np.concatenate(outputs, axis=0)
scores = 1 / (1 + np.exp(-outputs))
results = []
for item in scores:
    labels = []
    scores = []
    for idx, s in enumerate(item):
        labels.append(config["id2label"][str(idx)])
        scores.append(float(s))
    results.append({"labels": labels, "scores": scores})

res = []

for result in results:
    joined = list(zip(result['labels'], result['scores']))
    max_score = max(joined, key=lambda x: x[1])    
    res.append(max_score)

res
# [('toxic', 0.6553249955177307)]
```

# Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 48
- eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- warmup_ratio: 0.1


# Metrics (comparison with teacher model)

| Teacher (params)    |   Student (params)     | Set  (metric)     | Score (teacher)    |    Score (student)      |
|--------------------|-------------|----------|--------| --------|
| unitary/toxic-bert (110M) |  MiniLMv2-toxic-jigsaw-lite (23M)  | Test (ROC_AUC)  | 0.982677 | 0.9806 |

# Deployment

Check this [repository](https://github.com/minuva/toxicity-prediction-serverless) to see how to easily deploy this model in a serverless environment with fast CPU inference and light resource utilization.