Instructions to use RonTon05/step3_semisup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTon05/step3_semisup with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/step3_semisup") model = PhoBERTMultiTask.from_pretrained("RonTon05/step3_semisup") - Notebooks
- Google Colab
- Kaggle
File size: 1,291 Bytes
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library_name: transformers
license: agpl-3.0
base_model: RonTon05/model_content_V2_test
tags:
- generated_from_trainer
model-index:
- name: step3_semisup
results: []
---
<!-- 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. -->
# step3_semisup
This model is a fine-tuned version of [RonTon05/model_content_V2_test](https://huggingface.co/RonTon05/model_content_V2_test) on the None 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-06
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1150
- num_epochs: 15
### Training results
### Framework versions
- Transformers 5.12.1
- Pytorch 2.7.1+cu118
- Datasets 5.0.0
- Tokenizers 0.22.2
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