Instructions to use YaItco/DL2-HW2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YaItco/DL2-HW2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="YaItco/DL2-HW2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("YaItco/DL2-HW2") model = AutoModelForTokenClassification.from_pretrained("YaItco/DL2-HW2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
DL2-HW2
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: 0.1185
- Precision: 0.9268
- Recall: 0.9436
- F1: 0.9352
- Accuracy: 0.9815
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_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 |
|---|---|---|---|---|---|---|---|
| 0.0068 | 1.0 | 1250 | 0.1129 | 0.9256 | 0.9433 | 0.9343 | 0.9815 |
| 0.0036 | 2.0 | 2500 | 0.1187 | 0.9269 | 0.9442 | 0.9355 | 0.9818 |
| 0.0038 | 3.0 | 3750 | 0.1185 | 0.9268 | 0.9436 | 0.9352 | 0.9815 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.14.0+cu132
- Datasets 5.0.1
- Tokenizers 0.23.2
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Model tree for YaItco/DL2-HW2
Base model
BAAI/bge-small-en-v1.5