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---
language:
- en
- fr
- it
- es
- de
- nl
- pl
- ru
- pt
license: apache-2.0
tags:
- generated_from_trainer
- name-entity-recognition
- legal
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
base_model: distilbert-base-multilingual-cased
model-index:
- name: distilbert-base-multilingual-cased-mapa_coarse-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: lextreme
type: lextreme
config: mapa_coarse
split: test
args: mapa_coarse
metrics:
- type: precision
value: 0.7191116088092572
name: Precision
- type: recall
value: 0.6452855468095796
name: Recall
- type: f1
value: 0.6802012534204254
name: F1
- type: accuracy
value: 0.9878756336348935
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. -->
# distilbert-base-multilingual-cased-mapa_coarse-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: 0.0882
- Precision: 0.7191
- Recall: 0.6453
- F1: 0.6802
- Accuracy: 0.9879
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0248 | 1.0 | 1739 | 0.0528 | 0.7451 | 0.5805 | 0.6525 | 0.9871 |
| 0.0181 | 2.0 | 3478 | 0.0595 | 0.7369 | 0.5749 | 0.6459 | 0.9875 |
| 0.0121 | 3.0 | 5217 | 0.0499 | 0.7404 | 0.6280 | 0.6796 | 0.9879 |
| 0.0088 | 4.0 | 6956 | 0.0634 | 0.6912 | 0.6334 | 0.6610 | 0.9875 |
| 0.0072 | 5.0 | 8695 | 0.0625 | 0.7109 | 0.6478 | 0.6779 | 0.9880 |
| 0.0052 | 6.0 | 10434 | 0.0702 | 0.7098 | 0.6518 | 0.6796 | 0.9878 |
| 0.0041 | 7.0 | 12173 | 0.0733 | 0.7176 | 0.6429 | 0.6782 | 0.9878 |
| 0.0026 | 8.0 | 13912 | 0.0779 | 0.7198 | 0.6540 | 0.6853 | 0.9879 |
| 0.0019 | 9.0 | 15651 | 0.0875 | 0.7181 | 0.6419 | 0.6779 | 0.9877 |
| 0.0018 | 10.0 | 17390 | 0.0882 | 0.7191 | 0.6453 | 0.6802 | 0.9879 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2 |