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---
license: gpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: IceBERT-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
- name: Precision
type: precision
value: 0.8918518518518519
- name: Recall
type: recall
value: 0.8631855657784682
- name: F1
type: f1
value: 0.8772845953002611
- name: Accuracy
type: accuracy
value: 0.9851436434474428
---
<!-- 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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0830
- Precision: 0.8919
- Recall: 0.8632
- F1: 0.8773
- Accuracy: 0.9851
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0533 | 1.0 | 2904 | 0.0777 | 0.8773 | 0.8527 | 0.8648 | 0.9834 |
| 0.0271 | 2.0 | 5808 | 0.0794 | 0.8740 | 0.8537 | 0.8638 | 0.9835 |
| 0.0165 | 3.0 | 8712 | 0.0830 | 0.8919 | 0.8632 | 0.8773 | 0.9851 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
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