cardiffnlp/tweet_eval
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How to use aXhyra/presentation_irony_1234567 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/presentation_irony_1234567") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/presentation_irony_1234567")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/presentation_irony_1234567")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 |
|---|---|---|---|---|
| 0.5514 | 1.0 | 90 | 0.5917 | 0.6767 |
| 0.6107 | 2.0 | 180 | 0.6123 | 0.6730 |
| 0.1327 | 3.0 | 270 | 0.7463 | 0.6970 |
| 0.1068 | 4.0 | 360 | 0.9493 | 0.6746 |