metadata

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

bert-large-cantonese-sts

This model is a fine-tuned version of hon9kon9ize/bert-large-cantonese on the 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