Model Card for BERT hate offensive tweets
BERT base uncased trained on the data that can be found here: MartynaKopyta/hate_offensive_tweets to classify tweets as 0 - hate, 1 - offensive or 2 - neither.
You can find the notebook used for training in my GitHub repo: MartynaKopyta/BERT_FINE-TUNING.
Model Details
- Finetuned from model bert-base-uncased
Bias, Risks, and Limitations
The dataset was not big enough for BERT to learn to classify 3 classes accurately, it is right 3/4 times.
How to Get Started with the Model
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('MartynaKopyta/BERT_hate_offensive_tweets')
tokenizer = AutoTokenizer.from_pretrained('MartynaKopyta/BERT_hate_offensive_tweets')
Training Hyperparameters
- batch size:16
- learning rate:2e-5
- epochs:3
Evaluation
Accuracy: 0.779373368146214
Classification Report:
precision recall f1-score support
0 0.74 0.68 0.71 1532
1 0.85 0.88 0.87 1532
2 0.74 0.78 0.76 1532
accuracy 0.78 4596
macro avg 0.78 0.78 0.78 4596
weighted avg 0.78 0.78 0.78 4596
Confusion Matrix:
[[1043 96 393]
[ 169 1343 20]
[ 204 132 1196]]
MCC: 0.670
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