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---
language:
- it
tags:
- summarization
datasets:
- ARTeLab/ilpost
metrics:
- rouge
base_model: gsarti/it5-base
model-index:
- name: summarization_ilpost
results:
- task:
type: summarization
name: Summarization
dataset:
name: medical_questions_pairs
type: medical_questions_pairs
config: default
split: train
metrics:
- type: rouge
value: 12.5087
name: ROUGE-1
verified: true
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- type: rouge
value: 3.6796
name: ROUGE-2
verified: true
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- type: rouge
value: 11.0954
name: ROUGE-L
verified: true
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- type: rouge
value: 11.5897
name: ROUGE-LSUM
verified: true
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- type: loss
value: 3.0159499645233154
name: loss
verified: true
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- type: gen_len
value: 18.9961
name: gen_len
verified: true
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---
# summarization_ilpost
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.6020
- Rouge1: 33.7802
- Rouge2: 16.2953
- Rougel: 27.4797
- Rougelsum: 30.2273
- Gen Len: 45.3175
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3 |