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# stance_class_mod

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the dataset of 644 labeled tweets.
It achieves the following results on the evaluation set:
- Loss: 0.5008
- Accuracy: 0.8605

## 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: 30
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4898        | 1.0   | 13   | 0.4752          | 0.7752   |
| 0.4441        | 2.0   | 26   | 0.4355          | 0.7674   |
| 0.2363        | 3.0   | 39   | 0.5052          | 0.7984   |
| 0.2897        | 4.0   | 52   | 0.4911          | 0.7442   |
| 0.1451        | 5.0   | 65   | 0.3889          | 0.8295   |
| 0.0377        | 6.0   | 78   | 0.3988          | 0.8295   |
| 0.1236        | 7.0   | 91   | 0.4744          | 0.8605   |
| 0.1927        | 8.0   | 104  | 0.4761          | 0.8527   |
| 0.016         | 9.0   | 117  | 0.5158          | 0.8527   |
| 0.0145        | 10.0  | 130  | 0.5008          | 0.8605   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "stance_class_mod", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

stance\_class\_mod
==================


This model is a fine-tuned version of vinai/bertweet-base on the dataset of 644 labeled tweets.
It achieves the following results on the evaluation set:


* Loss: 0.5008
* Accuracy: 0.8605


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: 30
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 30
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #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: 30
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2