Downloads · 30 days
7
3% of all-time downloads
fine-finetuned
Stxlla/fine-finetuned
fine-finetuned is a machine learning model from Stxlla. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
276
Public
Repo size
7.8 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.9 GB · 100%
At a glance
- Library
- transformers
- License
- mit
- Model type
- m2m_100
- Access
- Public
- Created
- Dec 10, 2022
- Updated
- Dec 10, 2022
- SHA
- eacb4b74
Model card
From the Hugging Face model README
<!-- 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. -->
fine-finetuned
This model is a fine-tuned version of Stxlla/ko-en-following on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.1211
- eval_bleu: 61.2672
- eval_gen_len: 11.3556
- eval_runtime: 2042.0344
- eval_samples_per_second: 16.208
- eval_steps_per_second: 1.013
- epoch: 2.0
- step: 33098
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: 5
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
- Library
- transformers
- Type
- m2m_100
- License
- mit
- Created
- Dec 10, 2022
- Updated
- Dec 10, 2022