Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use mogmyij/yelp-model-3k-batch_size-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mogmyij/yelp-model-3k-batch_size-8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mogmyij/yelp-model-3k-batch_size-8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mogmyij/yelp-model-3k-batch_size-8") model = AutoModelForSequenceClassification.from_pretrained("mogmyij/yelp-model-3k-batch_size-8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
yelp-modoel-3k
This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0815
- Accuracy: 0.5857
- F1: 0.5879
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 375 | 0.9702 | 0.577 | 0.5698 |
| 1.0887 | 2.0 | 750 | 0.9836 | 0.5707 | 0.5762 |
| 0.7255 | 3.0 | 1125 | 1.0535 | 0.5823 | 0.5859 |
| 0.4467 | 4.0 | 1500 | 1.0815 | 0.5857 | 0.5879 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for mogmyij/yelp-model-3k-batch_size-8
Base model
google-bert/bert-base-uncased