justina/yelp_boba_reviews
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How to use justina/full-review-clf with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="justina/full-review-clf") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("justina/full-review-clf")
model = AutoModelForSequenceClassification.from_pretrained("justina/full-review-clf")This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on justina/yelp-boba-reviews 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 | F1 Macro | Aucpr Macro | Accuracy |
|---|---|---|---|---|---|---|
| 0.723 | 0.43 | 500 | 0.7576 | 0.5979 | 0.6652 | 0.6831 |
| 0.7307 | 0.87 | 1000 | 0.6862 | 0.6368 | 0.6752 | 0.7185 |
| 0.5828 | 1.3 | 1500 | 0.7398 | 0.6439 | 0.6661 | 0.7255 |
| 0.6236 | 1.73 | 2000 | 0.7878 | 0.6212 | 0.6690 | 0.7069 |
| 0.3739 | 2.16 | 2500 | 0.8138 | 0.6447 | 0.6752 | 0.7170 |
| 0.4235 | 2.6 | 3000 | 0.8048 | 0.6490 | 0.6673 | 0.7255 |
| 0.3684 | 3.03 | 3500 | 0.9615 | 0.6483 | 0.6715 | 0.7205 |
| 0.3243 | 3.46 | 4000 | 1.0931 | 0.6432 | 0.6632 | 0.7235 |