cardiffnlp/tweet_eval
Viewer • Updated • 201k • 35.7k • 143
How to use aXhyra/demo_hate_1234567 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/demo_hate_1234567") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/demo_hate_1234567")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/demo_hate_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:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 282 | 0.4850 | 0.7645 |
| 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 |
| 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 |
| 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 |