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# AkeyLegalBert_inScotus_and_Ledgar

This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4717

## 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: linear
- num_epochs: 7.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.0759        | 1.0   | 9878  | 3.9296          |
| 3.8398        | 2.0   | 19756 | 3.7768          |
| 3.7403        | 3.0   | 29634 | 3.6650          |
| 3.6514        | 4.0   | 39512 | 3.5936          |
| 3.6146        | 5.0   | 49390 | 3.5524          |
| 3.5412        | 6.0   | 59268 | 3.4985          |
| 3.5465        | 7.0   | 69146 | 3.4769          |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "AkeyLegalBert_inScotus_and_Ledgar", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

AkeyLegalBert\_inScotus\_and\_Ledgar
====================================


This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4717


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: linear
* num\_epochs: 7.0


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### 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: linear
* num\_epochs: 7.0 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-cc-by-sa-4.0 #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: linear
* num\_epochs: 7.0 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-cc-by-sa-4.0 #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: linear
* num\_epochs: 7.0### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1