File size: 2,269 Bytes
c931f56 ee2b1ab c931f56 ba1f31c ee2b1ab c931f56 ba1f31c c931f56 ba1f31c ee2b1ab ba1f31c c931f56 ba1f31c c931f56 0a9edbe c931f56 ee2b1ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
tags:
- generated_from_keras_callback
model-index:
- name: t5-small-MedicoSummarizer
results: []
---
# 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
|