cardiffnlp/tweet_eval
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How to use marcolatella/tweet_eval_bench with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="marcolatella/tweet_eval_bench") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("marcolatella/tweet_eval_bench")
model = AutoModelForSequenceClassification.from_pretrained("marcolatella/tweet_eval_bench")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 |
|---|---|---|---|---|
| 0.7022 | 1.0 | 1426 | 0.6581 | 0.7105 |
| 0.5199 | 2.0 | 2852 | 0.6835 | 0.706 |
| 0.2923 | 3.0 | 4278 | 0.7941 | 0.7075 |
| 0.1366 | 4.0 | 5704 | 1.0761 | 0.7115 |
| 0.0645 | 5.0 | 7130 | 1.5530 | 0.716 |