stanfordnlp/imdb
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How to use versae/gzipbert_imdb_rpe_250k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="versae/gzipbert_imdb_rpe_250k") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("versae/gzipbert_imdb_rpe_250k")
model = AutoModelForSequenceClassification.from_pretrained("versae/gzipbert_imdb_rpe_250k")This model is a fine-tuned version of versae/gzip-bert 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 |
|---|---|---|---|---|
| 0.003 | 1.0 | 1563 | 5.1727 | 0.5548 |
| 0.0061 | 2.0 | 3126 | 5.7975 | 0.5176 |
| 0.0056 | 3.0 | 4689 | 5.6762 | 0.5107 |
| 0.0019 | 4.0 | 6252 | 6.0355 | 0.5082 |
| 0.0043 | 5.0 | 7815 | 6.0866 | 0.5095 |
Base model
versae/gzip-bert