File size: 2,922 Bytes
42500ad 03e552a 42500ad 03e552a 42500ad 03e552a 42500ad 03e552a 42500ad 03e552a 42500ad |
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 |
---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nees-bert-base-portuguese-cased-finetuned-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. -->
# nees-bert-base-portuguese-cased-finetuned-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Precision: 0.5404
- Recall: 0.7230
- F1: 0.6185
- Accuracy: 0.9949
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0352 | 1.0 | 599 | 0.0212 | 0.0154 | 0.0034 | 0.0055 | 0.9956 |
| 0.0125 | 2.0 | 1198 | 0.0184 | 0.4039 | 0.2770 | 0.3287 | 0.9958 |
| 0.0099 | 3.0 | 1797 | 0.0141 | 0.4528 | 0.4865 | 0.4691 | 0.9958 |
| 0.0082 | 4.0 | 2396 | 0.0279 | 0.4558 | 0.5743 | 0.5082 | 0.9958 |
| 0.0074 | 5.0 | 2995 | 0.0138 | 0.5153 | 0.6824 | 0.5872 | 0.9960 |
| 0.0034 | 6.0 | 3594 | 0.0193 | 0.4934 | 0.6284 | 0.5527 | 0.9953 |
| 0.0046 | 7.0 | 4193 | 0.0190 | 0.5305 | 0.7635 | 0.6260 | 0.9960 |
| 0.0024 | 8.0 | 4792 | 0.0286 | 0.5670 | 0.6858 | 0.6208 | 0.9955 |
| 0.0024 | 9.0 | 5391 | 0.0270 | 0.5889 | 0.5709 | 0.5798 | 0.9953 |
| 0.0034 | 10.0 | 5990 | 0.0339 | 0.5623 | 0.6858 | 0.6180 | 0.9953 |
| 0.0015 | 11.0 | 6589 | 0.0373 | 0.6122 | 0.7095 | 0.6573 | 0.9947 |
| 0.001 | 12.0 | 7188 | 0.0361 | 0.5519 | 0.6284 | 0.5877 | 0.9950 |
| 0.0005 | 13.0 | 7787 | 0.0353 | 0.5658 | 0.6824 | 0.6187 | 0.9950 |
| 0.0007 | 14.0 | 8386 | 0.0355 | 0.5556 | 0.7264 | 0.6296 | 0.9948 |
| 0.0003 | 15.0 | 8985 | 0.0360 | 0.5404 | 0.7230 | 0.6185 | 0.9949 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|