# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("krm/BARTkrame-abstract")
model = AutoModelForSeq2SeqLM.from_pretrained("krm/BARTkrame-abstract")Quick Links
BARTkrame-abstract
This model is a fine-tuned version of krm/BARTkrame-abstract on the krm/for-ULPGL-Dissertation dataset. It achieves (15/10/2022) the following results on the evaluation set:
- Loss: 2.4196
- Rouge1: 0.2703
- Rouge2: 0.1334
- Rougel: 0.2392
- Rougelsum: 0.2419
Model description
This model is primarly a finetuned version of moussaKam/mbarthez.
Intended uses & limitations
More information needed
Training and evaluation data
We have used the krm/for-ULPGL-Dissertation dataset reduced to :
Training data : 5000 samples taken at random with seed=42.
Validation data : 100 samples taken at random with seed=42.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-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: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 0.1316 | 9.0 | 1250 | 2.3251 | 0.2505 | 0.1158 | 0.2150 | 0.2184 |
| 0.0894 | 10.0 | 2500 | 2.3467 | 0.2526 | 0.1073 | 0.2067 | 0.2124 |
| 0.045 | 11.0 | 3750 | 2.3742 | 0.2593 | 0.1211 | 0.2281 | 0.2308 |
| 0.0242 | 12.0 | 5000 | 2.4196 | 0.2703 | 0.1334 | 0.2392 | 0.2419 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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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="krm/BARTkrame-abstract")