README.md

metadata

language:
  - en

Text Classification GoEmotions

This is a quantized onnx model and is a fined-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large on the on the Jigsaw 1st Kaggle competition dataset using unitary/toxic-bert as teacher model.

Load the Model

import os
import numpy as np
import json

from tokenizers import Tokenizer
from onnxruntime import InferenceSession


# !git clone https://huggingface.co/Ngit/MiniLM-L6-toxic-all-labels-onnx

model_name = "Ngit/MiniLM-L6-toxic-all-labels-onnx"
tokenizer = Tokenizer.from_pretrained(model_name)
tokenizer.enable_padding(
    pad_token="<pad>",
    pad_id=1,
)
tokenizer.enable_truncation(max_length=256)
batch_size = 16

texts = ["This is pure trash",]
outputs = []
model = InferenceSession("MiniLM-L6-toxic-all-labels-onnx/model_optimized_quantized.onnx", providers=['CUDAExecutionProvider'])

with open(os.path.join("MiniLM-L6-toxic-all-labels-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], dtype=np.int64
                ),
                "attention_mask": np.vstack(
                    [encoding.attention_mask for encoding in encodings], dtype=np.int64
                ),
                "token_type_ids": np.vstack(
                    [encoding.type_ids for encoding in encodings], dtype=np.int64
                ),
            }

            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})

results

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-L6-H384-goemotions-v2-onnx (23M)	Test (ROC_AUC)	0.98636	0.98130

Deployment

Check this repository to see how to easily deploy this model in a serverless environment with fast CPU inference.