cardiffnlp/tweet_eval
Viewer • Updated • 201k • 40.9k • 144
How to use macampaz/clasificador-glue with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="macampaz/clasificador-glue") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("macampaz/clasificador-glue")
model = AutoModelForSequenceClassification.from_pretrained("macampaz/clasificador-glue")This model is a fine-tuned version of bert-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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 358 | 0.8826 | 0.6084 |
| 0.6268 | 2.0 | 716 | 0.6036 | 0.7079 |
| 0.3358 | 3.0 | 1074 | 1.3017 | 0.6837 |