text-sum-2
This model is a fine-tuned version of buianh0803/text-sum on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 1.6574
- Rouge1: 0.2485
- Rouge2: 0.1188
- Rougel: 0.2056
- Rougelsum: 0.2056
- Gen Len: 18.9991
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.7956 | 1.0 | 17945 | 1.6629 | 0.2481 | 0.1182 | 0.2053 | 0.2054 | 18.999 |
| 1.7865 | 2.0 | 35890 | 1.6576 | 0.2479 | 0.1181 | 0.2049 | 0.205 | 18.9987 |
| 1.7697 | 3.0 | 53835 | 1.6574 | 0.2485 | 0.1188 | 0.2056 | 0.2056 | 18.9991 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Dataset used to train buianh0803/text-sum-2
Evaluation results
- Rouge1 on cnn_dailymailtest set self-reported0.248
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("buianh0803/text-sum-2") model = AutoModelForSeq2SeqLM.from_pretrained("buianh0803/text-sum-2")