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--- |
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language: |
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- en |
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--- |
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# Text Classification GoEmotions |
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This is a quantized onnx model and 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. |
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# Load the Model |
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```py |
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import os |
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import numpy as np |
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import json |
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from tokenizers import Tokenizer |
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from onnxruntime import InferenceSession |
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# !git clone https://huggingface.co/Ngit/MiniLM-L6-toxic-all-labels-onnx |
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model_name = "Ngit/MiniLM-L6-toxic-all-labels-onnx" |
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tokenizer = Tokenizer.from_pretrained(model_name) |
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tokenizer.enable_padding( |
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pad_token="<pad>", |
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pad_id=1, |
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) |
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tokenizer.enable_truncation(max_length=256) |
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batch_size = 16 |
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texts = ["This is pure trash",] |
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outputs = [] |
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model = InferenceSession("MiniLM-L6-toxic-all-labels-onnx/model_optimized_quantized.onnx", providers=['CUDAExecutionProvider']) |
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with open(os.path.join("MiniLM-L6-toxic-all-labels-onnx", "config.json"), "r") as f: |
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config = json.load(f) |
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output_names = [output.name for output in model.get_outputs()] |
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input_names = [input.name for input in model.get_inputs()] |
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for subtexts in np.array_split(np.array(texts), len(texts) // batch_size + 1): |
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encodings = tokenizer.encode_batch(list(subtexts)) |
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inputs = { |
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"input_ids": np.vstack( |
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[encoding.ids for encoding in encodings], dtype=np.int64 |
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), |
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"attention_mask": np.vstack( |
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[encoding.attention_mask for encoding in encodings], dtype=np.int64 |
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), |
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"token_type_ids": np.vstack( |
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[encoding.type_ids for encoding in encodings], dtype=np.int64 |
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), |
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} |
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for input_name in input_names: |
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if input_name not in inputs: |
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raise ValueError(f"Input name {input_name} not found in inputs") |
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inputs = {input_name: inputs[input_name] for input_name in input_names} |
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output = np.squeeze( |
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np.stack( |
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model.run(output_names=output_names, input_feed=inputs) |
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), |
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axis=0, |
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) |
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outputs.append(output) |
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outputs = np.concatenate(outputs, axis=0) |
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scores = 1 / (1 + np.exp(-outputs)) |
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results = [] |
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for item in scores: |
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labels = [] |
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scores = [] |
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for idx, s in enumerate(item): |
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labels.append(config["id2label"][str(idx)]) |
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scores.append(float(s)) |
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results.append({"labels": labels, "scores": scores}) |
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results |
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``` |
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# Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 48 |
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- eval_batch_size: 48 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- warmup_ratio: 0.1 |
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# Metrics (comparison with teacher model) |
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| Teacher (params) | Student (params) | Set (metric) | Score (teacher) | Score (student) | |
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|--------------------|-------------|----------|--------| --------| |
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| unitary/toxic-bert (110M) | MiniLMv2-L6-H384-goemotions-v2-onnx (23M) | Test (ROC_AUC) | 0.98636 | 0.98130 | |
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# Deployment |
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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. |
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