cardiffnlp/tweet_eval
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How to use aXhyra/hate_trained_final with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/hate_trained_final") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/hate_trained_final")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/hate_trained_final")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 |
|---|---|---|---|---|
| 0.463 | 1.0 | 1125 | 0.5213 | 0.7384 |
| 0.3943 | 2.0 | 2250 | 0.5134 | 0.7534 |
| 0.3407 | 3.0 | 3375 | 0.5400 | 0.7666 |
| 0.3121 | 4.0 | 4500 | 0.5543 | 0.7698 |