# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("dtorber/NAS-bilingue")
model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/NAS-bilingue")Quick Links
NAS-bilingue
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.7187
- Rougelsum: 0.0922
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: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rougelsum |
|---|---|---|---|---|
| No log | 1.0 | 5 | 4.5936 | 0.0759 |
| No log | 2.0 | 10 | 4.4276 | 0.0759 |
| No log | 3.0 | 15 | 4.2936 | 0.0759 |
| No log | 4.0 | 20 | 4.1820 | 0.0759 |
| No log | 5.0 | 25 | 4.0896 | 0.0881 |
| No log | 6.0 | 30 | 4.0121 | 0.0970 |
| No log | 7.0 | 35 | 3.9451 | 0.0918 |
| No log | 8.0 | 40 | 3.8875 | 0.0922 |
| No log | 9.0 | 45 | 3.8395 | 0.0922 |
| No log | 10.0 | 50 | 3.8011 | 0.0922 |
| No log | 11.0 | 55 | 3.7707 | 0.0922 |
| No log | 12.0 | 60 | 3.7480 | 0.0922 |
| No log | 13.0 | 65 | 3.7320 | 0.0922 |
| No log | 14.0 | 70 | 3.7223 | 0.0922 |
| No log | 15.0 | 75 | 3.7187 | 0.0922 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="dtorber/NAS-bilingue")