# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("PranavY2k/my_distilbert_model")
model = AutoModelForSequenceClassification.from_pretrained("PranavY2k/my_distilbert_model")Quick Links
my_distilbert_model
This model is a fine-tuned version of distilbert-base-uncased on the rotten_tomatoes dataset. It achieves the following results on the evaluation set:
- Loss: 0.5593
- Accuracy: 0.8433
- F1: 0.8433
- Precision: 0.8438
- Recall: 0.8433
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4222 | 1.0 | 534 | 0.3821 | 0.8424 | 0.8421 | 0.8450 | 0.8424 |
| 0.2558 | 2.0 | 1068 | 0.4620 | 0.8433 | 0.8432 | 0.8445 | 0.8433 |
| 0.1609 | 3.0 | 1602 | 0.5593 | 0.8433 | 0.8433 | 0.8438 | 0.8433 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for PranavY2k/my_distilbert_model
Base model
distilbert/distilbert-base-uncasedDataset used to train PranavY2k/my_distilbert_model
Evaluation results
- Accuracy on rotten_tomatoestest set self-reported0.843
- F1 on rotten_tomatoestest set self-reported0.843
- Precision on rotten_tomatoestest set self-reported0.844
- Recall on rotten_tomatoestest set self-reported0.843
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PranavY2k/my_distilbert_model")