File size: 6,676 Bytes
aca3c9b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased_legal_ner_finetuned
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. -->
# bert-base-cased_legal_ner_finetuned
This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3018
- Law Precision: 0.7364
- Law Recall: 0.8261
- Law F1: 0.7787
- Law Number: 115
- Violated by Precision: 0.8525
- Violated by Recall: 0.6933
- Violated by F1: 0.7647
- Violated by Number: 75
- Violated on Precision: 0.4688
- Violated on Recall: 0.4286
- Violated on F1: 0.4478
- Violated on Number: 70
- Violation Precision: 0.6323
- Violation Recall: 0.7251
- Violation F1: 0.6755
- Violation Number: 491
- Overall Precision: 0.6524
- Overall Recall: 0.7097
- Overall F1: 0.6798
- Overall Accuracy: 0.9439
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Law Precision | Law Recall | Law F1 | Law Number | Violated by Precision | Violated by Recall | Violated by F1 | Violated by Number | Violated on Precision | Violated on Recall | Violated on F1 | Violated on Number | Violation Precision | Violation Recall | Violation F1 | Violation Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------------:|:------------------:|:--------------:|:------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 85 | 0.8046 | 0.0 | 0.0 | 0.0 | 115 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 70 | 0.0 | 0.0 | 0.0 | 491 | 0.0 | 0.0 | 0.0 | 0.7619 |
| No log | 2.0 | 170 | 0.4050 | 0.0 | 0.0 | 0.0 | 115 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 70 | 0.1835 | 0.2037 | 0.1931 | 491 | 0.1835 | 0.1332 | 0.1543 | 0.8819 |
| No log | 3.0 | 255 | 0.2861 | 0.6111 | 0.4783 | 0.5366 | 115 | 0.1818 | 0.0533 | 0.0825 | 75 | 0.4 | 0.0571 | 0.1000 | 70 | 0.4345 | 0.5540 | 0.4870 | 491 | 0.4479 | 0.4461 | 0.4470 | 0.9130 |
| No log | 4.0 | 340 | 0.2552 | 0.75 | 0.7043 | 0.7265 | 115 | 0.5625 | 0.36 | 0.4390 | 75 | 0.3429 | 0.1714 | 0.2286 | 70 | 0.4924 | 0.5927 | 0.5379 | 491 | 0.5256 | 0.5473 | 0.5362 | 0.9257 |
| No log | 5.0 | 425 | 0.2676 | 0.7154 | 0.7652 | 0.7395 | 115 | 0.7308 | 0.5067 | 0.5984 | 75 | 0.2778 | 0.1429 | 0.1887 | 70 | 0.5368 | 0.6090 | 0.5706 | 491 | 0.5664 | 0.5792 | 0.5727 | 0.9300 |
| 0.4786 | 6.0 | 510 | 0.2663 | 0.6767 | 0.7826 | 0.7258 | 115 | 0.7903 | 0.6533 | 0.7153 | 75 | 0.3684 | 0.4 | 0.3836 | 70 | 0.6155 | 0.7271 | 0.6667 | 491 | 0.6157 | 0.6977 | 0.6542 | 0.9366 |
| 0.4786 | 7.0 | 595 | 0.2352 | 0.6957 | 0.8348 | 0.7589 | 115 | 0.7941 | 0.72 | 0.7552 | 75 | 0.4242 | 0.4 | 0.4118 | 70 | 0.5799 | 0.7169 | 0.6412 | 491 | 0.6030 | 0.7057 | 0.6503 | 0.9412 |
| 0.4786 | 8.0 | 680 | 0.2728 | 0.6835 | 0.8261 | 0.7480 | 115 | 0.7857 | 0.7333 | 0.7586 | 75 | 0.3596 | 0.4571 | 0.4025 | 70 | 0.5916 | 0.7434 | 0.6588 | 491 | 0.5978 | 0.7284 | 0.6567 | 0.9415 |
| 0.4786 | 9.0 | 765 | 0.2952 | 0.7385 | 0.8348 | 0.7837 | 115 | 0.8088 | 0.7333 | 0.7692 | 75 | 0.5 | 0.5 | 0.5 | 70 | 0.6246 | 0.7352 | 0.6754 | 491 | 0.6466 | 0.7284 | 0.6850 | 0.9433 |
| 0.4786 | 10.0 | 850 | 0.3018 | 0.7364 | 0.8261 | 0.7787 | 115 | 0.8525 | 0.6933 | 0.7647 | 75 | 0.4688 | 0.4286 | 0.4478 | 70 | 0.6323 | 0.7251 | 0.6755 | 491 | 0.6524 | 0.7097 | 0.6798 | 0.9439 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|