
# t5-small-MedicoSummarizer

This model was fine-tuned on t5-small on 25,000 PubMed articles for 10 epochs.
It achieves the following results on the evaluation set:

## Training procedure
The inference engine doesn't do justice to its operation as the inference engine API doesn't work good for trainer checkpoints as the context limit is low in default for T5 which you can change while using it on backend of your application ! So, you should rather load it on the pipeline and just try it !

### Training hyperparameters

The following hyperparameters were used during training:
- batch_size = 16
- training_precision: float32
- epochs = 10
- learning_rate = 2e-5
- 

### Training results

|epoch|eval_loss         |eval_rouge1|eval_rouge2|eval_rougeL|eval_rougeLsum|eval_gen_len|
|-----|------------------|-----------|-----------|-----------|--------------|------------|
|1.0  |3.0605552196502686|0.302      |0.0693     |0.1841     |0.1842        |116.916     |
|2.0  |3.0079214572906494|0.3192     |0.0749     |0.1943     |0.1944        |122.076     |
|3.0  |2.9787817001342773|0.3209     |0.0758     |0.1957     |0.1958        |122.95      |
|4.0  |2.95868182182312  |0.3226     |0.0772     |0.1978     |0.1978        |123.593     |
|5.0  |2.943807601928711 |0.3186     |0.0743     |0.1959     |0.1959        |123.822     |
|6.0  |2.9342598915100098|0.3194     |0.0755     |0.1962     |0.1961        |123.834     |
|7.0  |2.927173376083374 |0.3205     |0.0758     |0.1967     |0.1968        |123.967     |
|8.0  |2.9225199222564697|0.3211     |0.0763     |0.1974     |0.1975        |124.178     |
|9.0  |2.9196181297302246|0.32       |0.0762     |0.1964     |0.1964        |124.136     |
|10.0 |2.9186391830444336|0.3209     |0.0766     |0.1965     |0.1965        |124.115     |

## Test Metrics
{'test_loss': 2.8919856548309326,
 'test_rouge1': 0.3207,
 'test_rouge2': 0.0741,
 'test_rougeL': 0.1955,
 'test_rougeLsum': 0.1955,
 'test_gen_len': 124.285,
 'test_runtime': 335.298,
 'test_samples_per_second': 5.965,
 'test_steps_per_second': 0.373}


### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-small-MedicoSummarizer", "results": []}]}
transformers tf tensorboard safetensors t5 text2text-generation generated_from_keras_callback autotrain_compatible endpoints_compatible text-generation-inference region:us