Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use abbysAI/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abbysAI/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abbysAI/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abbysAI/results") model = AutoModelForSequenceClassification.from_pretrained("abbysAI/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4349
- Mse: 1.4349
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse |
|---|---|---|---|---|
| 1.5497 | 1.0 | 150 | 1.5478 | 1.5478 |
| 1.1914 | 2.0 | 300 | 1.3738 | 1.3738 |
| 1.1 | 3.0 | 450 | 1.4349 | 1.4349 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for abbysAI/results
Base model
google-bert/bert-base-uncased