Instructions to use alex-tmfv/DZ_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alex-tmfv/DZ_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alex-tmfv/DZ_2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("alex-tmfv/DZ_2") model = AutoModelForTokenClassification.from_pretrained("alex-tmfv/DZ_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
DZ_2
This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0946
- Precision: 0.8969
- Recall: 0.9221
- F1: 0.9093
- Accuracy: 0.9810
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 |
|---|---|---|---|---|---|---|---|
| 0.0189 | 1.0 | 626 | 0.0908 | 0.8959 | 0.9170 | 0.9064 | 0.9809 |
| 0.0166 | 2.0 | 1252 | 0.0953 | 0.9035 | 0.9199 | 0.9116 | 0.9812 |
| 0.0138 | 3.0 | 1878 | 0.0946 | 0.8969 | 0.9221 | 0.9093 | 0.9810 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for alex-tmfv/DZ_2
Base model
BAAI/bge-small-en-v1.5