Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use mogmyij/yelp-model-6k-batch_size8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mogmyij/yelp-model-6k-batch_size8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mogmyij/yelp-model-6k-batch_size8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mogmyij/yelp-model-6k-batch_size8") model = AutoModelForSequenceClassification.from_pretrained("mogmyij/yelp-model-6k-batch_size8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
yelp-model-6k-batch_size8
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: 0.9301
- Accuracy: 0.618
- F1: 0.6181
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: 1e-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 |
|---|---|---|---|---|---|
| 1.1224 | 1.0 | 750 | 0.9045 | 0.589 | 0.5905 |
| 0.8153 | 2.0 | 1500 | 0.8807 | 0.609 | 0.6100 |
| 0.6604 | 3.0 | 2250 | 0.9192 | 0.618 | 0.6191 |
| 0.5564 | 4.0 | 3000 | 0.9301 | 0.618 | 0.6181 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for mogmyij/yelp-model-6k-batch_size8
Base model
google-bert/bert-base-uncased