
<!-- 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. -->

# twhin-bert-large-finetuned-fintwitter-classification

This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6936
- Accuracy: 0.8903
- F1: 0.8903
- Precision: 0.8903
- Recall: 0.8903

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 75   | 0.5087          | 0.7835   | 0.7594 | 0.7846    | 0.7835 |
| No log        | 2.0   | 150  | 0.3652          | 0.8685   | 0.8696 | 0.8735    | 0.8685 |
| No log        | 3.0   | 225  | 0.3452          | 0.8723   | 0.8739 | 0.8772    | 0.8723 |
| No log        | 4.0   | 300  | 0.3332          | 0.8823   | 0.8830 | 0.8840    | 0.8823 |
| No log        | 5.0   | 375  | 0.3618          | 0.8907   | 0.8909 | 0.8912    | 0.8907 |
| No log        | 6.0   | 450  | 0.3995          | 0.8802   | 0.8807 | 0.8814    | 0.8802 |
| 0.2712        | 7.0   | 525  | 0.4459          | 0.8886   | 0.8888 | 0.8889    | 0.8886 |
| 0.2712        | 8.0   | 600  | 0.5018          | 0.8903   | 0.8897 | 0.8894    | 0.8903 |
| 0.2712        | 9.0   | 675  | 0.5457          | 0.8836   | 0.8844 | 0.8860    | 0.8836 |
| 0.2712        | 10.0  | 750  | 0.5562          | 0.8723   | 0.8725 | 0.8750    | 0.8723 |
| 0.2712        | 11.0  | 825  | 0.6080          | 0.8827   | 0.8827 | 0.8828    | 0.8827 |
| 0.2712        | 12.0  | 900  | 0.5886          | 0.8853   | 0.8852 | 0.8851    | 0.8853 |
| 0.2712        | 13.0  | 975  | 0.6021          | 0.8874   | 0.8878 | 0.8890    | 0.8874 |
| 0.0383        | 14.0  | 1050 | 0.5960          | 0.8853   | 0.8849 | 0.8846    | 0.8853 |
| 0.0383        | 15.0  | 1125 | 0.6393          | 0.8815   | 0.8819 | 0.8827    | 0.8815 |
| 0.0383        | 16.0  | 1200 | 0.6803          | 0.8844   | 0.8852 | 0.8865    | 0.8844 |
| 0.0383        | 17.0  | 1275 | 0.6839          | 0.8915   | 0.8915 | 0.8914    | 0.8915 |
| 0.0383        | 18.0  | 1350 | 0.6968          | 0.8907   | 0.8901 | 0.8899    | 0.8907 |
| 0.0383        | 19.0  | 1425 | 0.6971          | 0.8865   | 0.8870 | 0.8878    | 0.8865 |
| 0.0122        | 20.0  | 1500 | 0.6936          | 0.8903   | 0.8903 | 0.8903    | 0.8903 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "twhin-bert-large-finetuned-fintwitter-classification", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

twhin-bert-large-finetuned-fintwitter-classification
====================================================


This model is a fine-tuned version of Twitter/twhin-bert-large on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6936
* Accuracy: 0.8903
* F1: 0.8903
* Precision: 0.8903
* Recall: 0.8903


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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2