cardiffnlp/tweet_eval
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How to use aXhyra/test_hate_trained_test with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/test_hate_trained_test") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/test_hate_trained_test")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/test_hate_trained_test")This model is a fine-tuned version of distilbert-base-uncased on the tweet_eval 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 | F1 |
|---|---|---|---|---|
| 0.4362 | 1.0 | 1125 | 0.5282 | 0.7369 |
| 0.3193 | 2.0 | 2250 | 0.6364 | 0.7571 |
| 0.1834 | 3.0 | 3375 | 1.0346 | 0.7625 |
| 0.0776 | 4.0 | 4500 | 1.1807 | 0.7692 |