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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-mapa_fine-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lextreme
type: lextreme
config: mapa_fine
split: test
args: mapa_fine
metrics:
- name: Precision
type: precision
value: 0.8763335204941044
- name: Recall
type: recall
value: 0.9115199299167762
- name: F1
type: f1
value: 0.8935804766335075
- name: Accuracy
type: accuracy
value: 0.9956876979901592
---
<!-- 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. -->
# distilbert-base-multilingual-cased-mapa_fine-ner
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0282
- Precision: 0.8763
- Recall: 0.9115
- F1: 0.8936
- Accuracy: 0.9957
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0244 | 1.0 | 1739 | 0.0202 | 0.8083 | 0.9314 | 0.8655 | 0.9941 |
| 0.0154 | 2.0 | 3478 | 0.0173 | 0.8813 | 0.9006 | 0.8908 | 0.9954 |
| 0.0118 | 3.0 | 5217 | 0.0161 | 0.8885 | 0.9131 | 0.9006 | 0.9960 |
| 0.0084 | 4.0 | 6956 | 0.0194 | 0.8485 | 0.9295 | 0.8871 | 0.9953 |
| 0.0069 | 5.0 | 8695 | 0.0219 | 0.8583 | 0.9198 | 0.8880 | 0.9953 |
| 0.0054 | 6.0 | 10434 | 0.0229 | 0.8622 | 0.9160 | 0.8883 | 0.9954 |
| 0.0032 | 7.0 | 12173 | 0.0248 | 0.8817 | 0.8979 | 0.8898 | 0.9956 |
| 0.0023 | 8.0 | 13912 | 0.0265 | 0.8900 | 0.9023 | 0.8961 | 0.9958 |
| 0.0018 | 9.0 | 15651 | 0.0275 | 0.8657 | 0.9137 | 0.8890 | 0.9954 |
| 0.0016 | 10.0 | 17390 | 0.0282 | 0.8763 | 0.9115 | 0.8936 | 0.9957 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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