Minuva Models
Collection
Fast and light models for conversational data.
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12 items
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Updated
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This is a quantized onnx model and is a fined-tuned version of MiniLMv2-L6-H384 on the on the Jigsaw 1st Kaggle competition dataset using unitary/toxic-bert as teacher model. The original model can be found here
Install from source:
python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
model = ORTModelForSequenceClassification.from_pretrained('minuva/MiniLMv2-toxic-jigsaw-onnx', provider="CPUExecutionProvider")
tokenizer = AutoTokenizer.from_pretrained('minuva/MiniLMv2-toxic-jigsaw-onnx', use_fast=True, model_max_length=256, truncation=True, padding='max_length')
pipe = pipeline(task='text-classification', model=model, tokenizer=tokenizer, )
texts = ["This is pure trash",]
pipe(texts)
# [{'label': 'toxic', 'score': 0.736885666847229}]
A lighter solution for deployment
pip install tokenizers
pip install onnxruntime
git clone https://huggingface.co./minuva/MiniLMv2-toxic-jigsaw-onnx
import os
import numpy as np
import json
from tokenizers import Tokenizer
from onnxruntime import InferenceSession
model_name = "minuva/MiniLMv2-toxic-jigsaw-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-onnx/model_optimized_quantized.onnx", providers=['CUDAExecutionProvider'])
with open(os.path.join("MiniLMv2-toxic-jigsaw-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', 0.736885666847229)]
The following hyperparameters were used during training:
Teacher (params) | Student (params) | Set (metric) | Score (teacher) | Score (student) |
---|---|---|---|---|
unitary/toxic-bert (110M) | MiniLMv2-toxic-jigsaw-onnx (23M) | Test (ROC_AUC) | 0.98636 | 0.98130 |
Check out fast-nlp-text-toxicity repository for a FastAPI based server to deploy this model in CPU devices.