---
license: cc-by-4.0
base_model: hon9kon9ize/bert-large-cantonese
tags:
- generated_from_trainer
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
model-index:
  - name: Cantonese Semantic Textual Similarity BERT based on hon9kon9ize/bert-large-cantonese-sts
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.8195601142712411
          - type: spearman_cosine
            value: 0.8107244990045813
          - type: pearson_manhattan
            value: 0.8227349515965701
          - type: spearman_manhattan
            value: 0.8106624105549446
          - type: pearson_euclidean
            value: 0.8224444134336916
          - type: spearman_euclidean
            value: 0.810580167108645
          - type: pearson_dot
            value: 0.8197330940854836
          - type: spearman_dot
            value: 0.8107833210821748
---

<!-- 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-large-cantonese-sts

This model is a fine-tuned version of [hon9kon9ize/bert-large-cantonese](https://huggingface.co/hon9kon9ize/bert-large-cantonese) on the [hon9kon9ize/yue-sts](https://huggingface.co/datasets/hon9kon9ize/yue-sts) dataset.

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10

### Training results



### Framework versions

- Transformers 4.43.3
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1