## reformer-clm

This casual language model was trained from scratch on CNN/Dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7783

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.8321        | 1.0   | 18412  | 3.8074          |
| 3.4965        | 2.0   | 36824  | 3.4223          |
| 3.1927        | 3.0   | 55236  | 3.0815          |
| 3.046         | 4.0   | 73648  | 2.9270          |
| 2.9781        | 5.0   | 92060  | 2.8515          |
| 2.9398        | 6.0   | 110472 | 2.8082          |
| 2.9293        | 7.0   | 128884 | 2.7904          |
| 2.9212        | 8.0   | 147296 | 2.7817          |
| 2.9169        | 9.0   | 165708 | 2.7787          |
| 2.9197        | 10.0  | 184120 | 2.7783          |


### Framework versions

- Transformers 4.6.1
- Pytorch 1.9.0
- Datasets 1.2.1
- Tokenizers 0.10.3

{}
transformers pytorch reformer text-generation autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #reformer #text-generation #autotrain_compatible #endpoints_compatible #region-us 

reformer-clm
------------


This casual language model was trained from scratch on CNN/Dailymail dataset.
It achieves the following results on the evaluation set:


* Loss: 2.7783


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.6.1
* Pytorch 1.9.0
* Datasets 1.2.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.6.1
* Pytorch 1.9.0
* Datasets 1.2.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #reformer #text-generation #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.6.1
* Pytorch 1.9.0
* Datasets 1.2.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #reformer #text-generation #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.6.1
* Pytorch 1.9.0
* Datasets 1.2.1
* Tokenizers 0.10.3