|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
- name-entity-recognition |
|
- legal |
|
datasets: |
|
- lextreme |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: distilbert-base-multilingual-cased-mapa_coarse-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: lextreme |
|
type: lextreme |
|
config: mapa_coarse |
|
split: test |
|
args: mapa_coarse |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.7191116088092572 |
|
- name: Recall |
|
type: recall |
|
value: 0.6452855468095796 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6802012534204254 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9878756336348935 |
|
language: |
|
- en |
|
- fr |
|
- it |
|
- es |
|
- de |
|
- nl |
|
- pl |
|
- ru |
|
- pt |
|
--- |
|
|
|
<!-- 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 |
