Instructions to use smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed_v1") model = AutoModelForSequenceClassification.from_pretrained("smartiros/BERT_for_sentiment_50k_2_epochs_preprocessed_v1") - Notebooks
- Google Colab
- Kaggle
tmp9eavpdw4
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1333
- Train Accuracy: 0.9487
- Validation Loss: 0.7282
- Validation Accuracy: 0.7929
- Epoch: 1
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:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.3768 | 0.8296 | 0.4746 | 0.8159 | 0 |
| 0.1333 | 0.9487 | 0.7282 | 0.7929 | 1 |
Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Tokenizers 0.11.6
- Downloads last month
- 7