---
license: afl-3.0
base_model: masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: ewc_stabilised
  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. -->

# ewc_stabilised

This model is a fine-tuned version of [masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1396
- F1: 0.8317
- Precision: 0.8305
- Recall: 0.8328
- Accuracy: 0.9605

## 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: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.3184        | 0.9993 | 701  | 0.1480          | 0.7895 | 0.7950    | 0.7841 | 0.9511   |
| 0.1333        | 2.0    | 1403 | 0.1271          | 0.8195 | 0.8148    | 0.8242 | 0.9578   |
| 0.0975        | 2.9993 | 2104 | 0.1241          | 0.8289 | 0.8254    | 0.8324 | 0.9598   |
| 0.0744        | 4.0    | 2806 | 0.1293          | 0.8307 | 0.8313    | 0.8300 | 0.9603   |
| 0.0596        | 4.9964 | 3505 | 0.1396          | 0.8317 | 0.8305    | 0.8328 | 0.9605   |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.4.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1