Quick Links
AraBART-traffics-summarization
This model is a fine-tuned version of moussaKam/AraBART on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5301
- Rouge1: 0.8041
- Rouge2: 0.7628
- Rougel: 0.8027
- Rougelsum: 0.8035
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 326 | 0.5848 | 0.7780 | 0.7365 | 0.7773 | 0.7779 |
| 1.0582 | 2.0 | 652 | 0.5345 | 0.7951 | 0.7535 | 0.7942 | 0.7947 |
| 1.0582 | 3.0 | 978 | 0.5332 | 0.7964 | 0.7536 | 0.7955 | 0.7960 |
| 0.5142 | 4.0 | 1304 | 0.5217 | 0.8016 | 0.7579 | 0.8009 | 0.8015 |
| 0.3681 | 5.0 | 1630 | 0.5301 | 0.8041 | 0.7628 | 0.8027 | 0.8035 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Downloads last month
- 4
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Amdalotaibi/AraBART-traffics-summarization-2
Base model
moussaKam/AraBART
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Amdalotaibi/AraBART-traffics-summarization-2") model = AutoModelForSeq2SeqLM.from_pretrained("Amdalotaibi/AraBART-traffics-summarization-2", device_map="auto")