File size: 3,505 Bytes
3706b51 |
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 99 100 101 102 103 104 105 106 107 |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-cfv
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.807683615819209
- name: Recall
type: recall
value: 0.8212316176470589
- name: F1
type: f1
value: 0.8144012760624361
- name: Accuracy
type: accuracy
value: 0.974075543714453
---
<!-- 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-cfv
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1851
- Precision: 0.8077
- Recall: 0.8212
- F1: 0.8144
- Accuracy: 0.9741
## 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: 4e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 17
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 347 | 0.1278 | 0.7284 | 0.7475 | 0.7378 | 0.9646 |
| 0.1176 | 2.0 | 694 | 0.1212 | 0.7509 | 0.7806 | 0.7654 | 0.9681 |
| 0.0453 | 3.0 | 1041 | 0.1156 | 0.8062 | 0.8116 | 0.8089 | 0.9730 |
| 0.0453 | 4.0 | 1388 | 0.1270 | 0.8081 | 0.8031 | 0.8056 | 0.9720 |
| 0.0233 | 5.0 | 1735 | 0.1298 | 0.8145 | 0.8231 | 0.8187 | 0.9746 |
| 0.0145 | 6.0 | 2082 | 0.1431 | 0.7950 | 0.8091 | 0.8020 | 0.9728 |
| 0.0145 | 7.0 | 2429 | 0.1501 | 0.8103 | 0.8166 | 0.8135 | 0.9734 |
| 0.009 | 8.0 | 2776 | 0.1553 | 0.8118 | 0.8157 | 0.8138 | 0.9738 |
| 0.0061 | 9.0 | 3123 | 0.1572 | 0.7891 | 0.8084 | 0.7986 | 0.9720 |
| 0.0061 | 10.0 | 3470 | 0.1589 | 0.8142 | 0.8196 | 0.8169 | 0.9739 |
| 0.005 | 11.0 | 3817 | 0.1671 | 0.8092 | 0.8148 | 0.8120 | 0.9733 |
| 0.0032 | 12.0 | 4164 | 0.1716 | 0.8066 | 0.8139 | 0.8102 | 0.9733 |
| 0.0031 | 13.0 | 4511 | 0.1767 | 0.8025 | 0.8169 | 0.8096 | 0.9731 |
| 0.0031 | 14.0 | 4858 | 0.1756 | 0.8096 | 0.8217 | 0.8156 | 0.9741 |
| 0.0023 | 15.0 | 5205 | 0.1845 | 0.8109 | 0.8157 | 0.8133 | 0.9739 |
| 0.0018 | 16.0 | 5552 | 0.1850 | 0.8090 | 0.8203 | 0.8146 | 0.9739 |
| 0.0018 | 17.0 | 5899 | 0.1851 | 0.8077 | 0.8212 | 0.8144 | 0.9741 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|