---
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