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Add evaluation results on the default config and train split of aslg_pc12 (#1)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - aslg_pc12
metrics:
  - bleu
  - sacrebleu
  - bertscore
base_model: t5-small
pipeline_tag: translation
model-index:
  - name: t5_small_gloss_merged_dataset_adj_adv
    results:
      - task:
          type: translation
          name: Translation
        dataset:
          name: aslg_pc12
          type: aslg_pc12
          config: default
          split: train
        metrics:
          - type: bleu
            value: 68.5164
            name: BLEU
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.33915433287620544
            name: loss
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTRiNmFlZTU1YjE0ZWJiYzQ2MjljZDBmODg1Nzg0MDQyM2Y3ZTJlMWFlMTcyZjYzZTBhNzY1ZjBiYjIxZTIyMSIsInZlcnNpb24iOjF9.YO6wVCBAhvDA1EeuoAUJLLLg3AIkrFhCGw4E8uqHzRXKIZQOvgeEUoUP5LXfq5vuzMmb93bhXoIxvdfNhKkbAw
          - type: gen_len
            value: 15.5783
            name: gen_len
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDViZTE5NmY2OWJjNDFjOTRkZDQwOGZmMmY1OGQzOTU0OWI5Y2RjMGMyYzQ5MDVlYTZjZWU1ZGI2NmI2NTBjYyIsInZlcnNpb24iOjF9.K4k2lCYC4jIa-zm7lWf9tymABXu6VrJrHP9HVIjHDcgY0DLiAI_IdiByWFLtYC1cyppL98BG_9SYs5RkJvi7Ag

t5_small_gloss_merged_dataset_adj_adv

This model is a fine-tuned version of t5-small on an unknown 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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0

Training results

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1