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# lesson-summarization

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0801

## Model description

More information needed

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 2.8198        | 3.12   | 200   | 2.8048          |
| 2.5358        | 6.25   | 400   | 2.6645          |
| 2.333         | 9.38   | 600   | 2.6123          |
| 2.2096        | 12.5   | 800   | 2.5807          |
| 2.0783        | 15.62  | 1000  | 2.5703          |
| 1.9919        | 18.75  | 1200  | 2.5653          |
| 1.89          | 21.88  | 1400  | 2.5602          |
| 1.7865        | 25.0   | 1600  | 2.5650          |
| 1.7149        | 28.12  | 1800  | 2.5812          |
| 1.6651        | 31.25  | 2000  | 2.5813          |
| 1.5662        | 34.38  | 2200  | 2.5997          |
| 1.5333        | 37.5   | 2400  | 2.6097          |
| 1.4336        | 40.62  | 2600  | 2.6389          |
| 1.3986        | 43.75  | 2800  | 2.6564          |
| 1.352         | 46.88  | 3000  | 2.6720          |
| 1.3072        | 50.0   | 3200  | 2.6863          |
| 1.2773        | 53.12  | 3400  | 2.6931          |
| 1.2079        | 56.25  | 3600  | 2.7350          |
| 1.1768        | 59.38  | 3800  | 2.7521          |
| 1.1749        | 62.5   | 4000  | 2.7553          |
| 1.0857        | 65.62  | 4200  | 2.7921          |
| 1.0883        | 68.75  | 4400  | 2.7840          |
| 1.0307        | 71.88  | 4600  | 2.8110          |
| 1.0255        | 75.0   | 4800  | 2.8365          |
| 0.9992        | 78.12  | 5000  | 2.8358          |
| 0.9516        | 81.25  | 5200  | 2.8554          |
| 0.9363        | 84.38  | 5400  | 2.8742          |
| 0.91          | 87.5   | 5600  | 2.8923          |
| 0.895         | 90.62  | 5800  | 2.9057          |
| 0.8371        | 93.75  | 6000  | 2.9234          |
| 0.8588        | 96.88  | 6200  | 2.9443          |
| 0.8237        | 100.0  | 6400  | 2.9612          |
| 0.8147        | 103.12 | 6600  | 2.9633          |
| 0.7936        | 106.25 | 6800  | 2.9641          |
| 0.7883        | 109.38 | 7000  | 2.9711          |
| 0.7589        | 112.5  | 7200  | 2.9744          |
| 0.7277        | 115.62 | 7400  | 2.9879          |
| 0.7505        | 118.75 | 7600  | 2.9974          |
| 0.705         | 121.88 | 7800  | 3.0033          |
| 0.7111        | 125.0  | 8000  | 3.0032          |
| 0.7005        | 128.12 | 8200  | 3.0055          |
| 0.6961        | 131.25 | 8400  | 3.0168          |
| 0.6543        | 134.38 | 8600  | 3.0339          |
| 0.6482        | 137.5  | 8800  | 3.0312          |
| 0.6807        | 140.62 | 9000  | 3.0393          |
| 0.6365        | 143.75 | 9200  | 3.0413          |
| 0.648         | 146.88 | 9400  | 3.0461          |
| 0.6275        | 150.0  | 9600  | 3.0454          |
| 0.6284        | 153.12 | 9800  | 3.0552          |
| 0.6062        | 156.25 | 10000 | 3.0514          |
| 0.6312        | 159.38 | 10200 | 3.0487          |
| 0.6244        | 162.5  | 10400 | 3.0525          |
| 0.5792        | 165.62 | 10600 | 3.0547          |
| 0.5997        | 168.75 | 10800 | 3.0491          |
| 0.5972        | 171.88 | 11000 | 3.0542          |
| 0.5891        | 175.0  | 11200 | 3.0624          |
| 0.582         | 178.12 | 11400 | 3.0717          |
| 0.5934        | 181.25 | 11600 | 3.0683          |
| 0.5803        | 184.38 | 11800 | 3.0761          |
| 0.5724        | 187.5  | 12000 | 3.0777          |
| 0.6015        | 190.62 | 12200 | 3.0784          |
| 0.5874        | 193.75 | 12400 | 3.0792          |
| 0.5531        | 196.88 | 12600 | 3.0801          |
| 0.5863        | 200.0  | 12800 | 3.0801          |


### Framework versions

- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lesson-summarization", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

lesson-summarization
====================


This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.0801


Model description
-----------------


More information needed


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: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu116
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu116
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu116
* Datasets 2.12.0
* Tokenizers 0.13.3