# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("NicholasSynovic/forking-test")
model = AutoModelForSequenceClassification.from_pretrained("NicholasSynovic/forking-test")Quick Links
forking-test
This model is a fine-tuned version of bert-base-cased on the yelp_review_full 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.01 | 1 | 1.6473 | 0.202 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for NicholasSynovic/forking-test
Base model
google-bert/bert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NicholasSynovic/forking-test")