Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use alpsencer/bert-base-uncased-finetuned-rte-run_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alpsencer/bert-base-uncased-finetuned-rte-run_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alpsencer/bert-base-uncased-finetuned-rte-run_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alpsencer/bert-base-uncased-finetuned-rte-run_3") model = AutoModelForSequenceClassification.from_pretrained("alpsencer/bert-base-uncased-finetuned-rte-run_3") - Notebooks
- Google Colab
- Kaggle
bert-base-uncased-finetuned-rte-run_3
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: 0.6815
- Accuracy: 0.5957
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 20 | 0.6842 | 0.5632 |
| No log | 2.0 | 40 | 0.6815 | 0.5957 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for alpsencer/bert-base-uncased-finetuned-rte-run_3
Base model
google-bert/bert-base-uncased