cardiffnlp/tweet_eval
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How to use RS-course/model-safety-W1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="RS-course/model-safety-W1") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("RS-course/model-safety-W1")
model = AutoModelForSequenceClassification.from_pretrained("RS-course/model-safety-W1")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 | Accuracy |
|---|---|---|---|---|
| 0.2625 | 1.0 | 1490 | 1.0005 | 0.8006 |
| 0.1394 | 2.0 | 2980 | 1.2166 | 0.7863 |
| 0.0767 | 3.0 | 4470 | 1.3816 | 0.7772 |
Base model
distilbert/distilbert-base-uncased