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---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: legal-base-bert-NER
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. -->
# legal-base-bert-NER
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0195
- Precision: 0.9678
- Recall: 0.9748
- F1: 0.9712
- Classification Report: precision recall f1-score support
LOC 0.98 0.99 0.98 1837
MISC 0.92 0.93 0.93 922
ORG 0.96 0.97 0.96 1341
PER 0.99 0.99 0.99 1842
micro avg 0.97 0.97 0.97 5942
macro avg 0.96 0.97 0.97 5942
weighted avg 0.97 0.97 0.97 5942
## 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: 5e-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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Classification Report |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.0423 | 2.3 | 500 | 0.0195 | 0.9678 | 0.9748 | 0.9712 | precision recall f1-score support
LOC 0.98 0.99 0.98 1837
MISC 0.92 0.93 0.93 922
ORG 0.96 0.97 0.96 1341
PER 0.99 0.99 0.99 1842
micro avg 0.97 0.97 0.97 5942
macro avg 0.96 0.97 0.97 5942
weighted avg 0.97 0.97 0.97 5942
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### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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