tweets-hate-speech-detection/tweets_hate_speech_detection
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How to use Arvnd03/Hate-Speech-Detection-mpnet-basev2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Arvnd03/Hate-Speech-Detection-mpnet-basev2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Arvnd03/Hate-Speech-Detection-mpnet-basev2")
model = AutoModelForSequenceClassification.from_pretrained("Arvnd03/Hate-Speech-Detection-mpnet-basev2", device_map="auto")This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the tweets_hate_speech_detection dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.1144 | 1.0 | 1599 | 0.0955 | 0.9693 | 0.7337 |
| 0.072 | 2.0 | 3198 | 0.0849 | 0.9750 | 0.8030 |
| 0.0458 | 3.0 | 4797 | 0.0841 | 0.9764 | 0.8011 |
| 0.0156 | 4.0 | 6396 | 0.1829 | 0.9689 | 0.7762 |
| 0.012 | 5.0 | 7995 | 0.1904 | 0.9745 | 0.7758 |
| 0.0157 | 6.0 | 9594 | 0.1622 | 0.9758 | 0.7914 |
| 0.0068 | 7.0 | 11193 | 0.1741 | 0.9736 | 0.8005 |