cardiffnlp/tweet_eval
Viewer • Updated • 201k • 36.6k • 143
How to use aXhyra/hate_trained_1234567 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/hate_trained_1234567") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/hate_trained_1234567")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/hate_trained_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 |
|---|---|---|---|---|
| 0.4835 | 1.0 | 563 | 0.4881 | 0.7534 |
| 0.3236 | 2.0 | 1126 | 0.5294 | 0.7610 |
| 0.219 | 3.0 | 1689 | 0.6095 | 0.7717 |
| 0.1409 | 4.0 | 2252 | 0.7912 | 0.7751 |