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---
license: gpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
base_model: vesteinn/IceBERT
model-index:
- name: IceBERT-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
- type: precision
value: 0.8920083733530353
name: Precision
- type: recall
value: 0.8655753375552635
name: Recall
- type: f1
value: 0.8785930867192238
name: F1
- type: accuracy
value: 0.9855436530476731
name: Accuracy
---
<!-- 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: 0.0772
- Precision: 0.8920
- Recall: 0.8656
- F1: 0.8786
- Accuracy: 0.9855
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0519 | 1.0 | 2904 | 0.0731 | 0.8700 | 0.8564 | 0.8631 | 0.9832 |
| 0.026 | 2.0 | 5808 | 0.0749 | 0.8771 | 0.8540 | 0.8654 | 0.9840 |
| 0.0159 | 3.0 | 8712 | 0.0772 | 0.8920 | 0.8656 | 0.8786 | 0.9855 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|