Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cong1230/my_awesome_model")
model = AutoModelForSequenceClassification.from_pretrained("cong1230/my_awesome_model")Quick Links
my_awesome_model
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2473
- Accuracy: 0.9277
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 391 | 0.2318 | 0.9226 |
| 0.1113 | 2.0 | 782 | 0.2473 | 0.9277 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.7
- Tokenizers 0.15.0
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Model tree for cong1230/my_awesome_model
Base model
distilbert/distilbert-base-uncasedDataset used to train cong1230/my_awesome_model
Evaluation results
- Accuracy on imdbtest set self-reported0.928
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cong1230/my_awesome_model")