text-sum-3
This model is a fine-tuned version of buianh0803/text-sum-2 on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 1.6546
- Rouge1: 0.2475
- Rouge2: 0.1177
- Rougel: 0.2051
- Rougelsum: 0.2051
- Gen Len: 19.0
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: 0.001
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.8082 | 1.0 | 17945 | 1.6546 | 0.2475 | 0.1177 | 0.2051 | 0.2051 | 19.0 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Base model
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buianh0803/text-sum-2Dataset used to train buianh0803/text-sum-3
Evaluation results
- Rouge1 on cnn_dailymailtest set self-reported0.247
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("buianh0803/text-sum-3") model = AutoModelForSeq2SeqLM.from_pretrained("buianh0803/text-sum-3")