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atasoglu/turkish-base-bert-uncased-offenseval2020_tr

This is an offensive language detection model fine-tuned with coltekin/offenseval2020_tr dataset on ytu-ce-cosmos/turkish-base-bert-uncased.

Usage

Quick usage:

from transformers import pipeline
pipe = pipeline("text-classification", "atasoglu/turkish-base-bert-uncased-offenseval2020_tr")
print(pipe("bu bir test metnidir.", top_k=None))
# [{'label': 'NOT', 'score': 0.9970345497131348}, {'label': 'OFF', 'score': 0.0029654440004378557}]

Or:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id = "atasoglu/turkish-base-bert-uncased-offenseval2020_tr"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id).to(device)

@torch.no_grad
def predict(X):
    inputs = tokenizer(X, padding="max_length", truncation=True, max_length=256, return_tensors="pt")
    outputs = model.forward(**inputs.to(device))
    return torch.argmax(outputs.logits, dim=-1).tolist()

print(predict(["bu bir test metnidir."]))
# [0]

Test Results

Test results examined on the test split of fine-tuning dataset.

precision recall f1-score support
NOT 0.9162 0.9559 0.9356 2812
OFF 0.7912 0.6564 0.7176 716
accuracy 0.8951 3528
macro avg 0.8537 0.8062 0.8266 3528
weighted avg 0.8908 0.8951 0.8914 3528
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Model size
111M params
Tensor type
F32
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Dataset used to train atasoglu/turkish-base-bert-uncased-offenseval2020_tr