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# legalbert-large-1.7M-1_class_actions

This model is a fine-tuned version of [pile-of-law/legalbert-large-1.7M-1](https://huggingface.co/pile-of-law/legalbert-large-1.7M-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6901
- Accuracy: 0.55

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 150  | 0.6892          | 0.55     |
| No log        | 2.0   | 300  | 0.6913          | 0.55     |
| No log        | 3.0   | 450  | 0.7061          | 0.45     |
| 0.7143        | 4.0   | 600  | 0.6908          | 0.55     |
| 0.7143        | 5.0   | 750  | 0.6925          | 0.55     |
| 0.7143        | 6.0   | 900  | 0.6951          | 0.45     |
| 0.7164        | 7.0   | 1050 | 0.6893          | 0.55     |
| 0.7164        | 8.0   | 1200 | 0.6884          | 0.55     |
| 0.7164        | 9.0   | 1350 | 0.6910          | 0.55     |
| 0.7042        | 10.0  | 1500 | 0.6882          | 0.55     |
| 0.7042        | 11.0  | 1650 | 0.7099          | 0.45     |
| 0.7042        | 12.0  | 1800 | 0.6910          | 0.55     |
| 0.7042        | 13.0  | 1950 | 0.6910          | 0.55     |
| 0.6999        | 14.0  | 2100 | 0.6901          | 0.55     |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "legalbert-large-1.7M-1_class_actions", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

legalbert-large-1.7M-1\_class\_actions
======================================


This model is a fine-tuned version of pile-of-law/legalbert-large-1.7M-1 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6901
* Accuracy: 0.55


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3