stanfordnlp/imdb
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How to use versae/gzipbert_imdb_rpe with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="versae/gzipbert_imdb_rpe") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("versae/gzipbert_imdb_rpe")
model = AutoModelForSequenceClassification.from_pretrained("versae/gzipbert_imdb_rpe")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("versae/gzipbert_imdb_rpe")
model = AutoModelForSequenceClassification.from_pretrained("versae/gzipbert_imdb_rpe")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 |
|---|---|---|---|---|
| 0.6993 | 1.0 | 1563 | 0.6932 | 0.5 |
| 0.6964 | 2.0 | 3126 | 0.6959 | 0.5 |
| 0.6948 | 3.0 | 4689 | 0.6933 | 0.5 |
| 0.6949 | 4.0 | 6252 | 0.6937 | 0.5 |
| 0.6944 | 5.0 | 7815 | 0.6932 | 0.5 |
Base model
versae/gzip-bert
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="versae/gzipbert_imdb_rpe")