Instructions to use APredator/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use APredator/results with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("APredator/results") model = AutoModelForSeq2SeqLM.from_pretrained("APredator/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from APredator/results: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/APredator/results/resolve/main/README.md
- Command line
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hf download hf://APredator/results/README.md
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curl -L -o README.md https://huggingface.co/APredator/results/resolve/main/README.md
1.42 kB
metadata
license: mit
base_model: nafisehNik/mt5-persian-summary
tags:
- generated_from_trainer
datasets:
- pn_summary
model-index:
- name: results
results: []
results
This model is a fine-tuned version of nafisehNik/mt5-persian-summary on the pn_summary dataset. It achieves the following results on the evaluation set:
- Loss: 0.2449
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.28 | 1.0 | 18455 | 0.2519 |
| 0.2723 | 2.0 | 36910 | 0.2463 |
| 0.2656 | 3.0 | 55365 | 0.2449 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1