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---
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
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