# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Ciphur/bert-base-uncased_finetuned")
model = AutoModelForTokenClassification.from_pretrained("Ciphur/bert-base-uncased_finetuned")Quick Links
bert-base-uncased_finetuned
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: 0.2161
- Precision: 0.5951
- Recall: 0.6837
- F1: 0.6364
- Accuracy: 0.9288
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 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.2595 | 0.5269 | 0.6151 | 0.5676 | 0.9200 |
| No log | 2.0 | 250 | 0.2191 | 0.5975 | 0.6802 | 0.6362 | 0.9284 |
| No log | 3.0 | 375 | 0.2161 | 0.5951 | 0.6837 | 0.6364 | 0.9288 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Ciphur/bert-base-uncased_finetuned
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ciphur/bert-base-uncased_finetuned")