Summarization
Transformers
PyTorch
TensorBoard
French
English
bart
text2text-generation
Generated from Trainer
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Benjiccee/BART-CNN-Orangesum")
model = AutoModelForSeq2SeqLM.from_pretrained("Benjiccee/BART-CNN-Orangesum")Quick Links
BART-CNN-Orangesum
This model is a fine-tuned version of facebook/bart-large-cnn on the orange_sum dataset. It achieves the following results on the evaluation set:
- Loss: 1.6370
It aims at improving the quality of the summary generated on French texts
Model description
this is a fine tuning of the model 'facebook/bart-large-cnn' on the 'orange_sum' dataset gives better results in French while keeping the intrinsic qualities of the BART model
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9062 | 0.37 | 500 | 1.8412 |
| 1.6596 | 0.75 | 1000 | 1.6370 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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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="Benjiccee/BART-CNN-Orangesum")