---
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: 1.6148
- Accuracy: 0.6429
- F1: 0.6377
- Precision: 0.6600
- Recall: 0.6429

## 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: 5.314885705504048e-06
- train_batch_size: 32
- eval_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 10   | 2.4233          | 0.1429   | 0.0916 | 0.0802    | 0.1429 |
| No log        | 2.0   | 20   | 2.4147          | 0.1429   | 0.0920 | 0.0804    | 0.1429 |
| No log        | 3.0   | 30   | 2.4004          | 0.1667   | 0.1208 | 0.1178    | 0.1667 |
| No log        | 4.0   | 40   | 2.3840          | 0.1587   | 0.1192 | 0.1064    | 0.1587 |
| No log        | 5.0   | 50   | 2.3674          | 0.1667   | 0.1293 | 0.1176    | 0.1667 |
| No log        | 6.0   | 60   | 2.3469          | 0.1905   | 0.1467 | 0.1312    | 0.1905 |
| No log        | 7.0   | 70   | 2.3135          | 0.1984   | 0.1750 | 0.2072    | 0.1984 |
| No log        | 8.0   | 80   | 2.2775          | 0.2143   | 0.1964 | 0.2092    | 0.2143 |
| No log        | 9.0   | 90   | 2.2407          | 0.2302   | 0.2121 | 0.2260    | 0.2302 |
| No log        | 10.0  | 100  | 2.1807          | 0.3016   | 0.2911 | 0.3070    | 0.3016 |
| No log        | 11.0  | 110  | 2.1111          | 0.3413   | 0.3332 | 0.3604    | 0.3413 |
| No log        | 12.0  | 120  | 2.0411          | 0.4048   | 0.3799 | 0.3880    | 0.4048 |
| No log        | 13.0  | 130  | 1.9745          | 0.4444   | 0.4421 | 0.4658    | 0.4444 |
| No log        | 14.0  | 140  | 1.9188          | 0.5238   | 0.5207 | 0.5477    | 0.5238 |
| No log        | 15.0  | 150  | 1.8678          | 0.5635   | 0.5585 | 0.5787    | 0.5635 |
| No log        | 16.0  | 160  | 1.8253          | 0.5794   | 0.5685 | 0.5826    | 0.5794 |
| No log        | 17.0  | 170  | 1.7861          | 0.5952   | 0.5879 | 0.6104    | 0.5952 |
| No log        | 18.0  | 180  | 1.7471          | 0.6032   | 0.5896 | 0.6028    | 0.6032 |
| No log        | 19.0  | 190  | 1.7105          | 0.6190   | 0.6132 | 0.6383    | 0.6190 |
| No log        | 20.0  | 200  | 1.6786          | 0.6270   | 0.6221 | 0.6484    | 0.6270 |
| No log        | 21.0  | 210  | 1.6556          | 0.6508   | 0.6428 | 0.6627    | 0.6508 |
| No log        | 22.0  | 220  | 1.6385          | 0.6349   | 0.6302 | 0.6532    | 0.6349 |
| No log        | 23.0  | 230  | 1.6256          | 0.6349   | 0.6298 | 0.6546    | 0.6349 |
| No log        | 24.0  | 240  | 1.6173          | 0.6429   | 0.6377 | 0.6600    | 0.6429 |
| No log        | 25.0  | 250  | 1.6148          | 0.6429   | 0.6377 | 0.6600    | 0.6429 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0