Instructions to use drive087/mt5_news_sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drive087/mt5_news_sum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("drive087/mt5_news_sum") model = AutoModelForSeq2SeqLM.from_pretrained("drive087/mt5_news_sum") - Notebooks
- Google Colab
- Kaggle
new_merge_mt5
This model is a fine-tuned version of thanathorn/mt5-cpe-kmutt-thai-sentence-sum on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.3352
- eval_rouge1: 0.0868
- eval_rouge2: 0.015
- eval_rougeL: 0.0873
- eval_rougeLsum: 0.0866
- eval_gen_len: 18.9547
- eval_runtime: 160.0435
- eval_samples_per_second: 6.486
- eval_steps_per_second: 1.625
- epoch: 5.14
- step: 2000
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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5000
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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