stanfordnlp/imdb
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How to use dfurman/distilbert-base-uncased-imdb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="dfurman/distilbert-base-uncased-imdb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dfurman/distilbert-base-uncased-imdb")
model = AutoModelForSequenceClassification.from_pretrained("dfurman/distilbert-base-uncased-imdb")This model is a fine-tuned version of distilbert/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 | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2601 | 1.0 | 3125 | 0.3550 | 0.8857 | 0.8744 | 0.9709 | 0.7953 |
| 0.1842 | 2.0 | 6250 | 0.2355 | 0.9327 | 0.9327 | 0.9328 | 0.9326 |
| 0.1191 | 3.0 | 9375 | 0.3287 | 0.9311 | 0.9303 | 0.9417 | 0.9191 |
| 0.0452 | 4.0 | 12500 | 0.4053 | 0.9331 | 0.9337 | 0.9256 | 0.942 |
| 0.0299 | 5.0 | 15625 | 0.4367 | 0.9327 | 0.9336 | 0.9212 | 0.9463 |
Base model
distilbert/distilbert-base-uncased