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# stance_class_l

This model is a fine-tuned version of vinai/bertweet-base on the dataset of 804 labeled tweets on the cancer risk controversy of Roundup Weedkiller \. 
It classified the stance of an individual's tweet toward Bayer, Monsanto, or other relevant organizations in the crisis. 
Two stances are classified: (0) Aggressive, (1) Non-Aggressive (neutral and accommodative).
It achieves the following results on the evaluation set:
- Loss: 0.6084
- Accuracy: 0.8447

## 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: 2.924e-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3566        | 1.0   | 17   | 0.4855          | 0.7578   |
| 0.2532        | 2.0   | 34   | 0.3632          | 0.8509   |
| 0.2351        | 3.0   | 51   | 0.3773          | 0.8509   |
| 0.043         | 4.0   | 68   | 0.3553          | 0.8571   |
| 0.08          | 5.0   | 85   | 0.4682          | 0.8447   |
| 0.3089        | 6.0   | 102  | 0.4686          | 0.8509   |
| 0.035         | 7.0   | 119  | 0.5876          | 0.8323   |
| 0.0188        | 8.0   | 136  | 0.5469          | 0.8571   |
| 0.021         | 9.0   | 153  | 0.5022          | 0.8447   |
| 0.0533        | 10.0  | 170  | 0.5240          | 0.8385   |
| 0.0175        | 11.0  | 187  | 0.6352          | 0.8447   |
| 0.0106        | 12.0  | 204  | 0.5856          | 0.8447   |
| 1.9534        | 13.0  | 221  | 0.5938          | 0.8509   |
| 0.0143        | 14.0  | 238  | 0.6074          | 0.8447   |
| 0.0079        | 15.0  | 255  | 0.6084          | 0.8447   |


### 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_l", "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\_l
================


This model is a fine-tuned version of vinai/bertweet-base on the dataset of 804 labeled tweets on the cancer risk controversy of Roundup Weedkiller .
It classified the stance of an individual's tweet toward Bayer, Monsanto, or other relevant organizations in the crisis.
Two stances are classified: (0) Aggressive, (1) Non-Aggressive (neutral and accommodative).
It achieves the following results on the evaluation set:


* Loss: 0.6084
* Accuracy: 0.8447


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: 2.924e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### 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: 2.924e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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: 2.924e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


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