Text_Summarization
This model is a fine-tuned version of t5-small on the billsum dataset. It achieves the following results on the evaluation set:
- Loss: 2.7235
- Rouge1: 0.1324
- Rouge2: 0.0397
- Rougel: 0.1114
- Rougelsum: 0.111
- 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: 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 74 | 2.8406 | 0.1286 | 0.04 | 0.1087 | 0.1088 | 19.0 |
| No log | 2.0 | 148 | 2.7235 | 0.1324 | 0.0397 | 0.1114 | 0.111 | 19.0 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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Dataset used to train ThirdEyeData/Text_Summarization
Evaluation results
- Rouge1 on billsumself-reported0.132
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ThirdEyeData/Text_Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ThirdEyeData/Text_Summarization")