Instructions to use yegor04/hw02_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yegor04/hw02_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yegor04/hw02_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("yegor04/hw02_model") model = AutoModelForTokenClassification.from_pretrained("yegor04/hw02_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hw02_model
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: 0.0828
- Precision: 0.8814
- Recall: 0.9164
- F1: 0.8985
- Accuracy: 0.9798
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: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.057 | 1.0 | 625 | 0.0897 | 0.8659 | 0.9137 | 0.8891 | 0.9779 |
| 0.0445 | 2.0 | 1250 | 0.0839 | 0.8789 | 0.9148 | 0.8965 | 0.9794 |
| 0.0395 | 3.0 | 1875 | 0.0828 | 0.8814 | 0.9164 | 0.8985 | 0.9798 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.21.2
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Model tree for yegor04/hw02_model
Base model
BAAI/bge-small-en-v1.5