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license: apache-2.0
base_model: ethanyt/guwenbert-large
tags:
- generated_from_trainer
datasets:
- ched_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: guwenbert-large-CHED-Event Detection
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ched_ner
type: ched_ner
config: ched_ner
split: validation
args: ched_ner
metrics:
- name: Precision
type: precision
value: 0.7442799461641992
- name: Recall
type: recall
value: 0.8069066147859922
- name: F1
type: f1
value: 0.7743290548424737
- name: Accuracy
type: accuracy
value: 0.9666064635130461
---
<!-- 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. -->
# guwenbert-large-CHED-Event Detection
This model is a fine-tuned version of [ethanyt/guwenbert-large](https://huggingface.co/ethanyt/guwenbert-large) on the ched_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1905
- Precision: 0.7443
- Recall: 0.8069
- F1: 0.7743
- Accuracy: 0.9666
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 356 | 0.1420 | 0.6862 | 0.7573 | 0.72 | 0.9609 |
| 0.2304 | 2.0 | 712 | 0.1324 | 0.6907 | 0.7972 | 0.7401 | 0.9624 |
| 0.095 | 3.0 | 1068 | 0.1314 | 0.7268 | 0.7918 | 0.7579 | 0.9656 |
| 0.095 | 4.0 | 1424 | 0.1348 | 0.7248 | 0.7967 | 0.7590 | 0.9659 |
| 0.0613 | 5.0 | 1780 | 0.1525 | 0.7088 | 0.8147 | 0.7581 | 0.9635 |
| 0.0397 | 6.0 | 2136 | 0.1635 | 0.7224 | 0.8127 | 0.7649 | 0.9648 |
| 0.0397 | 7.0 | 2492 | 0.1693 | 0.7416 | 0.7986 | 0.7691 | 0.9662 |
| 0.0261 | 8.0 | 2848 | 0.1809 | 0.7338 | 0.8059 | 0.7682 | 0.9657 |
| 0.0164 | 9.0 | 3204 | 0.1904 | 0.7291 | 0.8127 | 0.7686 | 0.9655 |
| 0.0124 | 10.0 | 3560 | 0.1905 | 0.7443 | 0.8069 | 0.7743 | 0.9666 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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