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license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: philosophy_model
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. -->
# philosophy_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a small manually curated dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
- Rouge1: 0.81
- Rouge2: 0.8004
- Rougel: 0.8107
- Rougelsum: 0.809
- Gen Len: 18.5
## Model description
This model summarises passages on Indian philosophy.
Enter snippet from Hindu philosophy in text box on right. Click compute.
## Intended uses & limitations
More information needed
## Training and evaluation data
Dataset:130, train:100, test:30
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0056
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 13 | 2.2462 | 0.3632 | 0.1462 | 0.3114 | 0.3126 | 18.3333 |
| No log | 2.0 | 26 | 1.4611 | 0.459 | 0.3039 | 0.4178 | 0.4178 | 18.5667 |
| No log | 3.0 | 39 | 0.8323 | 0.5465 | 0.4259 | 0.5247 | 0.5208 | 17.1333 |
| No log | 4.0 | 52 | 0.4723 | 0.6161 | 0.5176 | 0.601 | 0.6004 | 18.3667 |
| No log | 5.0 | 65 | 0.3121 | 0.6812 | 0.6078 | 0.6747 | 0.6714 | 18.2333 |
| No log | 6.0 | 78 | 0.1546 | 0.7418 | 0.7023 | 0.7338 | 0.7313 | 18.0667 |
| No log | 7.0 | 91 | 0.1121 | 0.7832 | 0.763 | 0.7802 | 0.7789 | 18.5 |
| No log | 8.0 | 104 | 0.0699 | 0.8014 | 0.7882 | 0.8027 | 0.8009 | 18.5333 |
| No log | 9.0 | 117 | 0.0459 | 0.7958 | 0.7805 | 0.7946 | 0.7917 | 18.5 |
| No log | 10.0 | 130 | 0.0517 | 0.8091 | 0.7958 | 0.8105 | 0.809 | 18.4667 |
| No log | 11.0 | 143 | 0.0358 | 0.7994 | 0.7852 | 0.7973 | 0.7946 | 18.5 |
| No log | 12.0 | 156 | 0.0418 | 0.7799 | 0.7548 | 0.7747 | 0.7732 | 18.2667 |
| No log | 13.0 | 169 | 0.0107 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
| No log | 14.0 | 182 | 0.0065 | 0.8077 | 0.7971 | 0.8094 | 0.8075 | 18.5 |
| No log | 15.0 | 195 | 0.0178 | 0.808 | 0.796 | 0.8094 | 0.8075 | 18.3667 |
| No log | 16.0 | 208 | 0.0017 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
| No log | 17.0 | 221 | 0.0055 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
| No log | 18.0 | 234 | 0.0020 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
| No log | 19.0 | 247 | 0.0006 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
| No log | 20.0 | 260 | 0.0005 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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