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# legal_long_legal_test_sm

This model is a fine-tuned version of [saibo/legal-longformer-base-4096](https://huggingface.co/saibo/legal-longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7030
- Accuracy: 0.4788
- Precision: 0.5067
- Recall: 0.5045
- F1: 0.5056
- D-index: 1.1273

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 0.98  | 26   | 0.6940          | 0.4858   | 0.5366    | 0.1964 | 0.2876 | 1.1593  |
| No log        | 2.0   | 53   | 0.6931          | 0.5165   | 0.5674    | 0.3571 | 0.4384 | 1.2106  |
| No log        | 2.98  | 79   | 0.6912          | 0.5236   | 0.5294    | 0.8839 | 0.6622 | 1.1943  |
| No log        | 4.0   | 106  | 0.6932          | 0.4953   | 0.5174    | 0.6652 | 0.5820 | 1.1510  |
| No log        | 4.98  | 132  | 0.7097          | 0.5024   | 0.5389    | 0.4018 | 0.4604 | 1.1802  |
| No log        | 6.0   | 159  | 0.7527          | 0.5212   | 0.5349    | 0.7188 | 0.6133 | 1.1992  |
| No log        | 6.98  | 185  | 0.8177          | 0.5189   | 0.5510    | 0.4821 | 0.5143 | 1.2081  |
| No log        | 8.0   | 212  | 1.0477          | 0.4858   | 0.5254    | 0.2768 | 0.3626 | 1.1547  |
| No log        | 8.98  | 238  | 1.2084          | 0.4858   | 0.5192    | 0.3616 | 0.4263 | 1.1498  |
| No log        | 10.0  | 265  | 1.3307          | 0.5118   | 0.5594    | 0.3571 | 0.4360 | 1.2013  |
| No log        | 10.98 | 291  | 1.7030          | 0.4906   | 0.5213    | 0.4375 | 0.4757 | 1.1548  |
| No log        | 12.0  | 318  | 1.8288          | 0.5142   | 0.5341    | 0.6295 | 0.5779 | 1.1904  |
| No log        | 12.98 | 344  | 2.1054          | 0.4882   | 0.5198    | 0.4107 | 0.4589 | 1.1517  |
| No log        | 14.0  | 371  | 2.2934          | 0.4811   | 0.5076    | 0.5982 | 0.5492 | 1.1265  |
| No log        | 14.98 | 397  | 2.4791          | 0.5      | 0.5429    | 0.3393 | 0.4176 | 1.1792  |
| No log        | 16.0  | 424  | 2.4843          | 0.4858   | 0.5138    | 0.5    | 0.5068 | 1.1418  |
| No log        | 16.98 | 450  | 2.6494          | 0.4835   | 0.5141    | 0.4062 | 0.4539 | 1.1425  |
| No log        | 18.0  | 477  | 2.6552          | 0.4788   | 0.5072    | 0.4732 | 0.4896 | 1.1291  |
| 0.3174        | 18.98 | 503  | 2.6959          | 0.4764   | 0.5045    | 0.5    | 0.5022 | 1.1228  |
| 0.3174        | 19.62 | 520  | 2.7030          | 0.4788   | 0.5067    | 0.5045 | 0.5056 | 1.1273  |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "legal_long_legal_test_sm", "results": []}]}
transformers pytorch tensorboard longformer text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #longformer #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

legal\_long\_legal\_test\_sm
============================


This model is a fine-tuned version of saibo/legal-longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.7030
* Accuracy: 0.4788
* Precision: 0.5067
* Recall: 0.5045
* F1: 0.5056
* D-index: 1.1273


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #longformer #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #longformer #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2