Summarization
Transformers
PyTorch
TensorBoard
encoder-decoder
text2text-generation
ar
roberta
xlmroberta2xlmroberta
Abstractive Summarization
Generated from Trainer
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ahmeddbahaa/xlmroberta2xlmroberta-finetuned-ar-wikilingua")
model = AutoModelForSeq2SeqLM.from_pretrained("ahmeddbahaa/xlmroberta2xlmroberta-finetuned-ar-wikilingua", device_map="auto")Quick Links
xlmroberta2xlmroberta-finetuned-ar-wikilingua
This model is a fine-tuned version of on the wiki_lingua dataset. It achieves the following results on the evaluation set:
- Loss: 4.7757
- Rouge-1: 11.2
- Rouge-2: 1.96
- Rouge-l: 10.28
- Gen Len: 19.8
- Bertscore: 66.27
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|---|---|---|---|---|---|---|---|---|
| 8.03 | 1.0 | 312 | 7.3208 | 0.19 | 0.0 | 0.19 | 20.0 | 54.84 |
| 7.2309 | 2.0 | 624 | 7.1107 | 1.17 | 0.03 | 1.16 | 20.0 | 60.0 |
| 7.0752 | 3.0 | 936 | 7.0061 | 2.58 | 0.15 | 2.55 | 20.0 | 63.52 |
| 6.7538 | 4.0 | 1248 | 6.4189 | 5.75 | 0.46 | 5.55 | 19.95 | 62.83 |
| 6.1513 | 5.0 | 1560 | 5.8402 | 8.46 | 1.04 | 8.08 | 19.2 | 64.25 |
| 5.6639 | 6.0 | 1872 | 5.3938 | 8.62 | 1.17 | 8.16 | 19.28 | 64.81 |
| 5.2857 | 7.0 | 2184 | 5.0719 | 9.34 | 1.41 | 8.61 | 19.71 | 65.29 |
| 5.027 | 8.0 | 2496 | 4.9047 | 10.42 | 1.52 | 9.57 | 19.57 | 65.75 |
| 4.8747 | 9.0 | 2808 | 4.8032 | 10.79 | 1.71 | 9.91 | 19.42 | 66.2 |
| 4.7855 | 10.0 | 3120 | 4.7757 | 11.01 | 1.73 | 10.04 | 19.55 | 66.24 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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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="ahmeddbahaa/xlmroberta2xlmroberta-finetuned-ar-wikilingua")