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--- |
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language: |
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- en |
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- fr |
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- it |
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- es |
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- de |
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- nl |
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- pl |
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- ru |
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- pt |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- name-entity-recognition |
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- legal |
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datasets: |
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- lextreme |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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base_model: distilbert-base-multilingual-cased |
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model-index: |
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- name: distilbert-base-multilingual-cased-mapa_coarse-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: lextreme |
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type: lextreme |
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config: mapa_coarse |
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split: test |
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args: mapa_coarse |
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metrics: |
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- type: precision |
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value: 0.7191116088092572 |
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name: Precision |
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- type: recall |
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value: 0.6452855468095796 |
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name: Recall |
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- type: f1 |
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value: 0.6802012534204254 |
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name: F1 |
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- type: accuracy |
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value: 0.9878756336348935 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-multilingual-cased-mapa_coarse-ner |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the lextreme dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0882 |
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- Precision: 0.7191 |
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- Recall: 0.6453 |
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- F1: 0.6802 |
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- Accuracy: 0.9879 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0248 | 1.0 | 1739 | 0.0528 | 0.7451 | 0.5805 | 0.6525 | 0.9871 | |
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| 0.0181 | 2.0 | 3478 | 0.0595 | 0.7369 | 0.5749 | 0.6459 | 0.9875 | |
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| 0.0121 | 3.0 | 5217 | 0.0499 | 0.7404 | 0.6280 | 0.6796 | 0.9879 | |
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| 0.0088 | 4.0 | 6956 | 0.0634 | 0.6912 | 0.6334 | 0.6610 | 0.9875 | |
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| 0.0072 | 5.0 | 8695 | 0.0625 | 0.7109 | 0.6478 | 0.6779 | 0.9880 | |
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| 0.0052 | 6.0 | 10434 | 0.0702 | 0.7098 | 0.6518 | 0.6796 | 0.9878 | |
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| 0.0041 | 7.0 | 12173 | 0.0733 | 0.7176 | 0.6429 | 0.6782 | 0.9878 | |
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| 0.0026 | 8.0 | 13912 | 0.0779 | 0.7198 | 0.6540 | 0.6853 | 0.9879 | |
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| 0.0019 | 9.0 | 15651 | 0.0875 | 0.7181 | 0.6419 | 0.6779 | 0.9877 | |
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| 0.0018 | 10.0 | 17390 | 0.0882 | 0.7191 | 0.6453 | 0.6802 | 0.9879 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
