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---
library_name: transformers
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-classn-LinearAlg-finetuned-pred-span-width-2
results: []
---
<!-- 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. -->
# distilbert-classn-LinearAlg-finetuned-pred-span-width-2
This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6166
- Accuracy: 0.8333
- F1: 0.8321
- Precision: 0.8444
- Recall: 0.8333
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 5.0405 | 0.6849 | 50 | 2.4804 | 0.0556 | 0.0339 | 0.0253 | 0.0556 |
| 4.9397 | 1.3699 | 100 | 2.4514 | 0.0714 | 0.0438 | 0.0322 | 0.0714 |
| 4.8593 | 2.0548 | 150 | 2.4005 | 0.0794 | 0.0627 | 0.0607 | 0.0794 |
| 4.7519 | 2.7397 | 200 | 2.3404 | 0.1905 | 0.1699 | 0.1849 | 0.1905 |
| 4.7117 | 3.4247 | 250 | 2.2563 | 0.2698 | 0.2785 | 0.3828 | 0.2698 |
| 4.4979 | 4.1096 | 300 | 2.1144 | 0.3810 | 0.3625 | 0.3976 | 0.3810 |
| 4.1155 | 4.7945 | 350 | 1.9127 | 0.5635 | 0.5591 | 0.6445 | 0.5635 |
| 3.5796 | 5.4795 | 400 | 1.6802 | 0.6032 | 0.6019 | 0.7030 | 0.6032 |
| 3.0998 | 6.1644 | 450 | 1.4151 | 0.6984 | 0.6879 | 0.7921 | 0.6984 |
| 2.5176 | 6.8493 | 500 | 1.1687 | 0.7698 | 0.7665 | 0.7902 | 0.7698 |
| 1.9411 | 7.5342 | 550 | 0.9922 | 0.7619 | 0.7633 | 0.8083 | 0.7619 |
| 1.4025 | 8.2192 | 600 | 0.8374 | 0.8095 | 0.8088 | 0.8457 | 0.8095 |
| 1.0761 | 8.9041 | 650 | 0.7305 | 0.8175 | 0.8124 | 0.8412 | 0.8175 |
| 0.8084 | 9.5890 | 700 | 0.6920 | 0.8254 | 0.8202 | 0.8502 | 0.8254 |
| 0.5516 | 10.2740 | 750 | 0.6456 | 0.8333 | 0.8328 | 0.8705 | 0.8333 |
| 0.4201 | 10.9589 | 800 | 0.6497 | 0.8175 | 0.8102 | 0.8566 | 0.8175 |
| 0.2738 | 11.6438 | 850 | 0.5939 | 0.8333 | 0.8337 | 0.8524 | 0.8333 |
| 0.235 | 12.3288 | 900 | 0.6067 | 0.8413 | 0.8397 | 0.8641 | 0.8413 |
| 0.1387 | 13.0137 | 950 | 0.5975 | 0.8333 | 0.8306 | 0.8496 | 0.8333 |
| 0.1154 | 13.6986 | 1000 | 0.5704 | 0.8413 | 0.8389 | 0.8515 | 0.8413 |
| 0.0715 | 14.3836 | 1050 | 0.5859 | 0.8413 | 0.8397 | 0.8536 | 0.8413 |
| 0.0741 | 15.0685 | 1100 | 0.5732 | 0.8413 | 0.8393 | 0.8510 | 0.8413 |
| 0.0545 | 15.7534 | 1150 | 0.6005 | 0.8333 | 0.8310 | 0.8512 | 0.8333 |
| 0.0354 | 16.4384 | 1200 | 0.6069 | 0.8413 | 0.8398 | 0.8564 | 0.8413 |
| 0.0435 | 17.1233 | 1250 | 0.6056 | 0.8413 | 0.8389 | 0.8515 | 0.8413 |
| 0.0305 | 17.8082 | 1300 | 0.6066 | 0.8413 | 0.8393 | 0.8558 | 0.8413 |
| 0.02 | 18.4932 | 1350 | 0.6091 | 0.8333 | 0.8315 | 0.8427 | 0.8333 |
| 0.0271 | 19.1781 | 1400 | 0.6121 | 0.8333 | 0.8315 | 0.8427 | 0.8333 |
| 0.0211 | 19.8630 | 1450 | 0.6166 | 0.8333 | 0.8321 | 0.8444 | 0.8333 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
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