File size: 3,339 Bytes
ad26a72 90f8a79 ad26a72 90f8a79 ad26a72 90f8a79 ad26a72 90f8a79 ad26a72 90f8a79 ad26a72 90f8a79 ad26a72 90f8a79 ad26a72 |
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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- type: precision
value: 0.9427525378598769
name: Precision
- type: recall
value: 0.9533826994278021
name: Recall
- type: f1
value: 0.9480378211028366
name: F1
- type: accuracy
value: 0.9866957084829575
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1128
- Precision: 0.9428
- Recall: 0.9534
- F1: 0.9480
- Accuracy: 0.9867
## 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
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0937 | 1.0 | 1756 | 0.0660 | 0.9179 | 0.9332 | 0.9255 | 0.9825 |
| 0.0378 | 2.0 | 3512 | 0.0766 | 0.9246 | 0.9451 | 0.9348 | 0.9843 |
| 0.0245 | 3.0 | 5268 | 0.0667 | 0.9241 | 0.9409 | 0.9325 | 0.9843 |
| 0.017 | 4.0 | 7024 | 0.0712 | 0.9343 | 0.9505 | 0.9424 | 0.9863 |
| 0.0143 | 5.0 | 8780 | 0.0898 | 0.9366 | 0.9492 | 0.9428 | 0.9855 |
| 0.0049 | 6.0 | 10536 | 0.0964 | 0.9294 | 0.9482 | 0.9387 | 0.9853 |
| 0.0039 | 7.0 | 12292 | 0.1001 | 0.9353 | 0.9512 | 0.9432 | 0.9860 |
| 0.0036 | 8.0 | 14048 | 0.1002 | 0.9388 | 0.9522 | 0.9454 | 0.9862 |
| 0.0018 | 9.0 | 15804 | 0.1049 | 0.9363 | 0.9495 | 0.9428 | 0.9861 |
| 0.0019 | 10.0 | 17560 | 0.1191 | 0.9375 | 0.9497 | 0.9436 | 0.9849 |
| 0.0008 | 11.0 | 19316 | 0.1083 | 0.9396 | 0.9530 | 0.9463 | 0.9864 |
| 0.0003 | 12.0 | 21072 | 0.1064 | 0.9419 | 0.9530 | 0.9475 | 0.9864 |
| 0.0004 | 13.0 | 22828 | 0.1091 | 0.9448 | 0.9527 | 0.9487 | 0.9865 |
| 0.0006 | 14.0 | 24584 | 0.1132 | 0.9464 | 0.9542 | 0.9503 | 0.9867 |
| 0.0004 | 15.0 | 26340 | 0.1128 | 0.9428 | 0.9534 | 0.9480 | 0.9867 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|