
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlnet-base-cased_fold_10_binary_v1

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: 1.7782
- F1: 0.8137

## 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   | 288  | 0.3796          | 0.8145 |
| 0.4196        | 2.0   | 576  | 0.4319          | 0.7810 |
| 0.4196        | 3.0   | 864  | 0.6227          | 0.8002 |
| 0.231         | 4.0   | 1152 | 0.6258          | 0.7941 |
| 0.231         | 5.0   | 1440 | 1.0692          | 0.7866 |
| 0.1307        | 6.0   | 1728 | 1.1257          | 0.8005 |
| 0.0756        | 7.0   | 2016 | 1.2283          | 0.8072 |
| 0.0756        | 8.0   | 2304 | 1.3407          | 0.8061 |
| 0.0486        | 9.0   | 2592 | 1.5232          | 0.8059 |
| 0.0486        | 10.0  | 2880 | 1.6731          | 0.8053 |
| 0.0339        | 11.0  | 3168 | 1.6536          | 0.8087 |
| 0.0339        | 12.0  | 3456 | 1.7526          | 0.7996 |
| 0.019         | 13.0  | 3744 | 1.6662          | 0.7909 |
| 0.0237        | 14.0  | 4032 | 1.6028          | 0.8071 |
| 0.0237        | 15.0  | 4320 | 1.7627          | 0.7964 |
| 0.0078        | 16.0  | 4608 | 1.6513          | 0.8169 |
| 0.0078        | 17.0  | 4896 | 1.7795          | 0.8039 |
| 0.015         | 18.0  | 5184 | 1.8669          | 0.7935 |
| 0.015         | 19.0  | 5472 | 1.6288          | 0.8118 |
| 0.0124        | 20.0  | 5760 | 1.6630          | 0.8104 |
| 0.004         | 21.0  | 6048 | 1.7418          | 0.8167 |
| 0.004         | 22.0  | 6336 | 1.7651          | 0.8128 |
| 0.0043        | 23.0  | 6624 | 1.7279          | 0.8163 |
| 0.0043        | 24.0  | 6912 | 1.8177          | 0.8093 |
| 0.004         | 25.0  | 7200 | 1.7782          | 0.8137 |


### Framework versions

- Transformers 4.21.1
- 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_10_binary_v1", "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\_10\_binary\_v1
======================================


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: 1.7782
* F1: 0.8137


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 4.21.1
* 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 4.21.1
* 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 4.21.1
* 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 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1