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DL2_hw2_task3
vsinitsynav/DL2_hw2_task3
DL2_hw2_task3 is a token classification model from vsinitsynav. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Oct 13, 2025
- Updated
- Oct 14, 2025
- SHA
- e4fcd720
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
DL2_hw2_task3
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: 0.1247
- Precision: 0.8276
- Recall: 0.8802
- F1: 0.8531
- Accuracy: 0.9732
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 |
|---|---|---|---|---|---|---|---|
| 0.4932 | 1.0 | 625 | 0.2024 | 0.7167 | 0.7742 | 0.7444 | 0.9551 |
| 0.1943 | 2.0 | 1250 | 0.1387 | 0.8220 | 0.8671 | 0.8439 | 0.9712 |
| 0.1446 | 3.0 | 1875 | 0.1247 | 0.8276 | 0.8802 | 0.8531 | 0.9732 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.21.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Oct 13, 2025
- Updated
- Oct 14, 2025