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# xlnet-base-cased_fold_3_binary

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3616
- F1: 0.7758

## 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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 289  | 0.4668          | 0.7666 |
| 0.4142        | 2.0   | 578  | 0.4259          | 0.7631 |
| 0.4142        | 3.0   | 867  | 0.6744          | 0.7492 |
| 0.235         | 4.0   | 1156 | 0.8879          | 0.7678 |
| 0.235         | 5.0   | 1445 | 1.0036          | 0.7639 |
| 0.1297        | 6.0   | 1734 | 1.1427          | 0.7616 |
| 0.0894        | 7.0   | 2023 | 1.2126          | 0.7626 |
| 0.0894        | 8.0   | 2312 | 1.5098          | 0.7433 |
| 0.0473        | 9.0   | 2601 | 1.3616          | 0.7758 |
| 0.0473        | 10.0  | 2890 | 1.5966          | 0.7579 |
| 0.0325        | 11.0  | 3179 | 1.6669          | 0.7508 |
| 0.0325        | 12.0  | 3468 | 1.7401          | 0.7437 |
| 0.0227        | 13.0  | 3757 | 1.7797          | 0.7515 |
| 0.0224        | 14.0  | 4046 | 1.7349          | 0.7418 |
| 0.0224        | 15.0  | 4335 | 1.7527          | 0.7595 |
| 0.0152        | 16.0  | 4624 | 1.7492          | 0.7634 |
| 0.0152        | 17.0  | 4913 | 1.8178          | 0.7628 |
| 0.0117        | 18.0  | 5202 | 1.7736          | 0.7688 |
| 0.0117        | 19.0  | 5491 | 1.8449          | 0.7704 |
| 0.0055        | 20.0  | 5780 | 1.8687          | 0.7652 |
| 0.0065        | 21.0  | 6069 | 1.8083          | 0.7669 |
| 0.0065        | 22.0  | 6358 | 1.8568          | 0.7559 |
| 0.0054        | 23.0  | 6647 | 1.8760          | 0.7678 |
| 0.0054        | 24.0  | 6936 | 1.8948          | 0.7697 |
| 0.0048        | 25.0  | 7225 | 1.9109          | 0.7680 |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_3_binary", "results": []}]}
transformers pytorch tensorboard xlnet text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xlnet-base-cased\_fold\_3\_binary
=================================


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


* Loss: 1.3616
* F1: 0.7758


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: 25


### Training results



### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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: 25 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #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: 25 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #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: 25### Training results### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1