stanfordnlp/imdb
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How to use nosnelmil/RoBERTa-CompareTransformers-Imdb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="nosnelmil/RoBERTa-CompareTransformers-Imdb") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nosnelmil/RoBERTa-CompareTransformers-Imdb")
model = AutoModelForSequenceClassification.from_pretrained("nosnelmil/RoBERTa-CompareTransformers-Imdb", device_map="auto")This model is a fine-tuned version of roberta-base on the imdb 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 | Precision | Recall | F1 | Auroc |
|---|---|---|---|---|---|---|---|---|
| 0.1764 | 0.46 | 500 | 0.2698 | 0.9044 | 0.9044 | 0.9044 | 0.9044 | 0.9738 |
| 0.3348 | 0.91 | 1000 | 0.2755 | 0.9117 | 0.9117 | 0.9117 | 0.9117 | 0.9686 |
| 0.1478 | 1.37 | 1500 | 0.3275 | 0.9109 | 0.9109 | 0.9109 | 0.9109 | 0.9771 |
| 0.2051 | 1.83 | 2000 | 0.2575 | 0.9309 | 0.9309 | 0.9309 | 0.9309 | 0.9793 |
| 0.1435 | 2.29 | 2500 | 0.3140 | 0.9245 | 0.9245 | 0.9245 | 0.9245 | 0.9783 |
| 0.1425 | 2.74 | 3000 | 0.2691 | 0.9287 | 0.9287 | 0.9287 | 0.9287 | 0.9772 |
Base model
FacebookAI/roberta-base