cardiffnlp/tweet_eval
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How to use muhtasham/tiny-mlm-imdb-target-tweet with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="muhtasham/tiny-mlm-imdb-target-tweet") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("muhtasham/tiny-mlm-imdb-target-tweet")
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/tiny-mlm-imdb-target-tweet")This model is a fine-tuned version of muhtasham/tiny-mlm-imdb 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 | F1 |
|---|---|---|---|---|---|
| 1.159 | 4.9 | 500 | 0.9977 | 0.6364 | 0.6013 |
| 0.7514 | 9.8 | 1000 | 0.8549 | 0.7112 | 0.7026 |
| 0.5011 | 14.71 | 1500 | 0.8516 | 0.7032 | 0.6962 |
| 0.34 | 19.61 | 2000 | 0.9019 | 0.7059 | 0.7030 |
| 0.2258 | 24.51 | 2500 | 0.9722 | 0.7166 | 0.7164 |
| 0.1607 | 29.41 | 3000 | 1.0724 | 0.6979 | 0.6999 |
| 0.1127 | 34.31 | 3500 | 1.1435 | 0.7193 | 0.7169 |
| 0.0791 | 39.22 | 4000 | 1.2807 | 0.7059 | 0.7069 |
| 0.0568 | 44.12 | 4500 | 1.3849 | 0.7139 | 0.7159 |
| 0.0478 | 49.02 | 5000 | 1.5550 | 0.6925 | 0.7004 |