stanfordnlp/imdb
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How to use Realgon/left_padding40model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Realgon/left_padding40model") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Realgon/left_padding40model")
model = AutoModelForSequenceClassification.from_pretrained("Realgon/left_padding40model", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.0835 | 1.0 | 1563 | 0.9287 | 0.3934 |
| 0.0219 | 2.0 | 3126 | 0.9306 | 0.4228 |
| 0.0408 | 3.0 | 4689 | 0.9276 | 0.4133 |
| 0.0316 | 4.0 | 6252 | 0.9300 | 0.4801 |
| 0.0285 | 5.0 | 7815 | 0.9287 | 0.5290 |
| 0.0318 | 6.0 | 9378 | 0.9281 | 0.5954 |
| 0.0199 | 7.0 | 10941 | 0.9311 | 0.6008 |
| 0.0049 | 8.0 | 12504 | 0.9305 | 0.6087 |
| 0.0003 | 9.0 | 14067 | 0.9320 | 0.6529 |
| 0.0032 | 10.0 | 15630 | 0.9335 | 0.6508 |
Base model
distilbert/distilbert-base-uncased