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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-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-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: 0.8927
- Accuracy: 0.7698
- F1: 0.7669
- Precision: 0.7824
- Recall: 0.7698
## 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: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 4.8367 | 0.6849 | 50 | 2.4596 | 0.0794 | 0.0714 | 0.0958 | 0.0794 |
| 4.9882 | 1.3699 | 100 | 2.4445 | 0.0794 | 0.0672 | 0.0879 | 0.0794 |
| 4.8852 | 2.0548 | 150 | 2.4040 | 0.0873 | 0.0904 | 0.1342 | 0.0873 |
| 4.7843 | 2.7397 | 200 | 2.3744 | 0.1429 | 0.1481 | 0.2396 | 0.1429 |
| 4.752 | 3.4247 | 250 | 2.3612 | 0.1032 | 0.1062 | 0.1491 | 0.1032 |
| 4.6277 | 4.1096 | 300 | 2.3446 | 0.1587 | 0.1570 | 0.1976 | 0.1587 |
| 4.4488 | 4.7945 | 350 | 2.2895 | 0.1746 | 0.1760 | 0.2217 | 0.1746 |
| 4.4244 | 5.4795 | 400 | 2.2383 | 0.2302 | 0.2282 | 0.3192 | 0.2302 |
| 3.9882 | 6.1644 | 450 | 2.1156 | 0.2381 | 0.2338 | 0.2955 | 0.2381 |
| 3.7244 | 6.8493 | 500 | 1.9715 | 0.3730 | 0.3763 | 0.4472 | 0.3730 |
| 3.2134 | 7.5342 | 550 | 1.8718 | 0.4206 | 0.3950 | 0.4017 | 0.4206 |
| 2.9113 | 8.2192 | 600 | 1.7821 | 0.4127 | 0.4249 | 0.5411 | 0.4127 |
| 2.4754 | 8.9041 | 650 | 1.6155 | 0.4841 | 0.4828 | 0.5088 | 0.4841 |
| 1.9316 | 9.5890 | 700 | 1.4559 | 0.5714 | 0.5673 | 0.5759 | 0.5714 |
| 1.6141 | 10.2740 | 750 | 1.2770 | 0.6429 | 0.6300 | 0.6630 | 0.6429 |
| 1.1867 | 10.9589 | 800 | 1.1722 | 0.6508 | 0.6439 | 0.6649 | 0.6508 |
| 0.9252 | 11.6438 | 850 | 1.0998 | 0.6825 | 0.6830 | 0.7084 | 0.6825 |
| 0.764 | 12.3288 | 900 | 1.0359 | 0.7143 | 0.7181 | 0.7575 | 0.7143 |
| 0.5821 | 13.0137 | 950 | 0.9742 | 0.7302 | 0.7288 | 0.7554 | 0.7302 |
| 0.4689 | 13.6986 | 1000 | 0.9252 | 0.7460 | 0.7459 | 0.7639 | 0.7460 |
| 0.3578 | 14.3836 | 1050 | 0.9470 | 0.7302 | 0.7281 | 0.7663 | 0.7302 |
| 0.2932 | 15.0685 | 1100 | 0.9157 | 0.7222 | 0.7181 | 0.7552 | 0.7222 |
| 0.2262 | 15.7534 | 1150 | 0.8814 | 0.7540 | 0.7525 | 0.7723 | 0.7540 |
| 0.2127 | 16.4384 | 1200 | 0.8926 | 0.7381 | 0.7349 | 0.7488 | 0.7381 |
| 0.1445 | 17.1233 | 1250 | 0.8955 | 0.7698 | 0.7672 | 0.7891 | 0.7698 |
| 0.1183 | 17.8082 | 1300 | 0.8903 | 0.7698 | 0.7648 | 0.8007 | 0.7698 |
| 0.0757 | 18.4932 | 1350 | 0.8743 | 0.7698 | 0.7656 | 0.7831 | 0.7698 |
| 0.0939 | 19.1781 | 1400 | 0.8584 | 0.8016 | 0.8032 | 0.8200 | 0.8016 |
| 0.0705 | 19.8630 | 1450 | 0.8636 | 0.7857 | 0.7849 | 0.7965 | 0.7857 |
| 0.0605 | 20.5479 | 1500 | 0.8750 | 0.7778 | 0.7743 | 0.7831 | 0.7778 |
| 0.0467 | 21.2329 | 1550 | 0.8834 | 0.7778 | 0.7762 | 0.7898 | 0.7778 |
| 0.0777 | 21.9178 | 1600 | 0.8909 | 0.7698 | 0.7668 | 0.7809 | 0.7698 |
| 0.0349 | 22.6027 | 1650 | 0.8852 | 0.7698 | 0.7669 | 0.7824 | 0.7698 |
| 0.0442 | 23.2877 | 1700 | 0.8873 | 0.7698 | 0.7669 | 0.7824 | 0.7698 |
| 0.0253 | 23.9726 | 1750 | 0.8917 | 0.7698 | 0.7669 | 0.7824 | 0.7698 |
| 0.0335 | 24.6575 | 1800 | 0.8927 | 0.7698 | 0.7669 | 0.7824 | 0.7698 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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