Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use shrabani0708/test-trainer-sd-123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shrabani0708/test-trainer-sd-123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shrabani0708/test-trainer-sd-123")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shrabani0708/test-trainer-sd-123") model = AutoModelForSequenceClassification.from_pretrained("shrabani0708/test-trainer-sd-123", device_map="auto") - Notebooks
- Google Colab
- Kaggle
test-trainer-sd-123
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: 0.6958
- Accuracy: 0.8603
- F1: 0.9055
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: 5e-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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.4489 | 0.8260 | 0.8849 |
| 0.5612 | 2.0 | 918 | 0.3560 | 0.8578 | 0.8990 |
| 0.3362 | 3.0 | 1377 | 0.6958 | 0.8603 | 0.9055 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for shrabani0708/test-trainer-sd-123
Base model
google-bert/bert-base-uncased