File size: 2,005 Bytes
c871ccb bfa99ce c871ccb bfa99ce c871ccb 58d558b 025b5c7 58d558b c871ccb |
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 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
tasks: summarization
license: apache-2.0
tags:
- generated_from_trainer
- Summarization
model-index:
- name: distilbart-podimo-data-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-podimo-data-5
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1325
## Model description
model | rouge1 | rouge2 | rougeL | rougeLsum
--- | --- | --- | --- |---
sshleifer/distilbart-cnn-12-6 | 0.202654 | 0.025766 | 0.123072 | 0.130183
emmyapi/distilbart-podimo-data-3 | 0.235147 | 0.047087 | 0.151535 | 0.161782
emmyapi/distilbart-podimo-data-4 | 0.236926 | 0.048327 | 0.153539 | 0.165026
emmyapi/distilbart-podimo-data-5 | 0.259024 | 0.061665 | 0.167187 | 0.178399
emmyapi/distilbart-podimo-data-7 | 0.298888 | 0.059900 | 0.159479 | 0.185049
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3477 | 3.33 | 500 | 3.7027 |
| 2.6286 | 6.66 | 1000 | 3.6995 |
| 2.0718 | 10.0 | 1500 | 3.8868 |
| 1.7806 | 13.33 | 2000 | 4.1325 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|