Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use stelebe67/yelp_review_classifer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stelebe67/yelp_review_classifer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stelebe67/yelp_review_classifer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stelebe67/yelp_review_classifer") model = AutoModelForSequenceClassification.from_pretrained("stelebe67/yelp_review_classifer") - Notebooks
- Google Colab
- Kaggle
yelp_review_classifer
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0774
- Accuracy: 0.553
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: 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 16 | 1.5590 | 0.354 |
| No log | 2.0 | 32 | 1.3821 | 0.47 |
| No log | 3.0 | 48 | 1.1736 | 0.536 |
| No log | 4.0 | 64 | 1.0961 | 0.553 |
| No log | 5.0 | 80 | 1.0774 | 0.553 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for stelebe67/yelp_review_classifer
Base model
google-bert/bert-base-cased