Text Classification
Transformers
TensorBoard
Safetensors
distilbert
yelp
review-quality
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("RLau33/yelpreviewproject")
model = AutoModelForSequenceClassification.from_pretrained("RLau33/yelpreviewproject")Quick Links
yelpreviewproject
This model was trained from scratch on an unknown dataset.
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RLau33/yelpreviewproject")