stanfordnlp/imdb
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How to use yujiepan/bert-base-uncased-imdb-92.8 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="yujiepan/bert-base-uncased-imdb-92.8") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("yujiepan/bert-base-uncased-imdb-92.8")
model = AutoModelForSequenceClassification.from_pretrained("yujiepan/bert-base-uncased-imdb-92.8", device_map="auto")This model is a fine-tuned version of yujiepan/bert-base-uncased-imdb 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 |
|---|---|---|---|---|
| No log | 0.5 | 391 | 0.4331 | 0.9122 |
| 0.0385 | 1.0 | 782 | 0.3080 | 0.9150 |
| 0.0654 | 1.5 | 1173 | 0.3286 | 0.9164 |
| 0.0636 | 2.0 | 1564 | 0.4296 | 0.9184 |
| 0.0636 | 2.5 | 1955 | 0.5366 | 0.9170 |
| 0.0344 | 3.0 | 2346 | 0.4581 | 0.9172 |
| 0.0285 | 3.5 | 2737 | 0.5553 | 0.9164 |
| 0.0163 | 4.0 | 3128 | 0.5055 | 0.9194 |
| 0.0104 | 4.5 | 3519 | 0.5432 | 0.9264 |
| 0.0104 | 5.0 | 3910 | 0.5357 | 0.9268 |
| 0.0034 | 5.5 | 4301 | 0.5457 | 0.9249 |
| 0.0046 | 6.0 | 4692 | 0.5326 | 0.9282 |