stanfordnlp/imdb
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How to use muhtasham/mini-vanilla-target-imdb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="muhtasham/mini-vanilla-target-imdb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("muhtasham/mini-vanilla-target-imdb")
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/mini-vanilla-target-imdb")This model is a fine-tuned version of google/bert_uncased_L-4_H-256_A-4 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 |
|---|---|---|---|---|---|
| 0.4272 | 0.64 | 500 | 0.2066 | 0.92 | 0.9583 |
| 0.299 | 1.28 | 1000 | 0.2608 | 0.8906 | 0.9422 |
| 0.2533 | 1.92 | 1500 | 0.1706 | 0.9337 | 0.9657 |
| 0.2126 | 2.56 | 2000 | 0.3601 | 0.8576 | 0.9233 |
| 0.1913 | 3.2 | 2500 | 0.3955 | 0.8594 | 0.9244 |
| 0.1541 | 3.84 | 3000 | 0.1432 | 0.9484 | 0.9735 |
| 0.1432 | 4.48 | 3500 | 0.2027 | 0.9346 | 0.9662 |
| 0.1256 | 5.12 | 4000 | 0.3797 | 0.8898 | 0.9417 |
| 0.1026 | 5.75 | 4500 | 0.4773 | 0.8753 | 0.9335 |