---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: modernbert-base-wnut17-english-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.5518248175182482
    - name: Recall
      type: recall
      value: 0.35032437442076
    - name: F1
      type: f1
      value: 0.4285714285714286
    - name: Accuracy
      type: accuracy
      value: 0.9457125758741558
---

<!-- 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. -->

# modernbert-base-wnut17-english-ner

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5510
- Precision: 0.5518
- Recall: 0.3503
- F1: 0.4286
- Accuracy: 0.9457

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 107  | 0.3280          | 0.2601    | 0.0778 | 0.1198 | 0.9292   |
| No log        | 2.0   | 214  | 0.2790          | 0.5609    | 0.2048 | 0.3001 | 0.9377   |
| No log        | 3.0   | 321  | 0.2860          | 0.4403    | 0.2595 | 0.3265 | 0.9394   |
| No log        | 4.0   | 428  | 0.3018          | 0.4534    | 0.3698 | 0.4074 | 0.9442   |
| 0.1707        | 5.0   | 535  | 0.3328          | 0.4742    | 0.3661 | 0.4132 | 0.9445   |
| 0.1707        | 6.0   | 642  | 0.4206          | 0.5119    | 0.3401 | 0.4087 | 0.9445   |
| 0.1707        | 7.0   | 749  | 0.4242          | 0.5238    | 0.3364 | 0.4097 | 0.9449   |
| 0.1707        | 8.0   | 856  | 0.4635          | 0.5624    | 0.3133 | 0.4024 | 0.9447   |
| 0.1707        | 9.0   | 963  | 0.4705          | 0.5432    | 0.3494 | 0.4253 | 0.9461   |
| 0.0052        | 10.0  | 1070 | 0.4557          | 0.4962    | 0.3652 | 0.4207 | 0.9456   |
| 0.0052        | 11.0  | 1177 | 0.5900          | 0.5956    | 0.3234 | 0.4192 | 0.9448   |
| 0.0052        | 12.0  | 1284 | 0.5206          | 0.5701    | 0.3429 | 0.4282 | 0.9456   |
| 0.0052        | 13.0  | 1391 | 0.5535          | 0.5805    | 0.3309 | 0.4215 | 0.9455   |
| 0.0052        | 14.0  | 1498 | 0.5098          | 0.5297    | 0.3559 | 0.4257 | 0.9457   |
| 0.0011        | 15.0  | 1605 | 0.5543          | 0.5681    | 0.3401 | 0.4255 | 0.9457   |
| 0.0011        | 16.0  | 1712 | 0.5394          | 0.5512    | 0.3494 | 0.4277 | 0.9456   |
| 0.0011        | 17.0  | 1819 | 0.5492          | 0.5577    | 0.3448 | 0.4261 | 0.9457   |
| 0.0011        | 18.0  | 1926 | 0.5412          | 0.5489    | 0.3540 | 0.4304 | 0.9458   |
| 0.0008        | 19.0  | 2033 | 0.5472          | 0.5485    | 0.3513 | 0.4282 | 0.9456   |
| 0.0008        | 20.0  | 2140 | 0.5510          | 0.5518    | 0.3503 | 0.4286 | 0.9457   |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0