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# bert-base-uncased-Twitter_Sentiment_Analysis_v2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5809
- Accuracy: 0.8522
- Weighted f1: 0.8507
- Micro f1: 0.8522
- Macro f1: 0.8007
- Weighted recall: 0.8522
- Micro recall: 0.8522
- Macro recall: 0.8006
- Weighted precision: 0.8503
- Micro precision: 0.8522
- Macro precision: 0.8025

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.3896        | 1.0   | 183  | 0.3073          | 0.8022   | 0.7743      | 0.8022   | 0.6960   | 0.8022          | 0.8022       | 0.6914       | 0.8053             | 0.8022          | 0.7880          |
| 0.2488        | 2.0   | 366  | 0.2937          | 0.8474   | 0.8409      | 0.8474   | 0.7880   | 0.8474          | 0.8474       | 0.7747       | 0.8429             | 0.8474          | 0.8132          |
| 0.1766        | 3.0   | 549  | 0.3115          | 0.8398   | 0.8298      | 0.8398   | 0.7750   | 0.8398          | 0.8398       | 0.7613       | 0.8355             | 0.8398          | 0.8111          |
| 0.1253        | 4.0   | 732  | 0.3354          | 0.8487   | 0.8406      | 0.8487   | 0.7843   | 0.8487          | 0.8487       | 0.7695       | 0.8448             | 0.8487          | 0.8167          |
| 0.09          | 5.0   | 915  | 0.4137          | 0.8474   | 0.8447      | 0.8474   | 0.7930   | 0.8474          | 0.8474       | 0.7932       | 0.8455             | 0.8474          | 0.7985          |
| 0.0677        | 6.0   | 1098 | 0.4872          | 0.8494   | 0.8491      | 0.8494   | 0.7977   | 0.8494          | 0.8494       | 0.8068       | 0.8511             | 0.8494          | 0.7930          |
| 0.0501        | 7.0   | 1281 | 0.4959          | 0.8576   | 0.8556      | 0.8576   | 0.8066   | 0.8576          | 0.8576       | 0.8063       | 0.8558             | 0.8576          | 0.8105          |
| 0.0415        | 8.0   | 1464 | 0.5412          | 0.8515   | 0.8505      | 0.8515   | 0.8003   | 0.8515          | 0.8515       | 0.8017       | 0.8503             | 0.8515          | 0.8000          |
| 0.0323        | 9.0   | 1647 | 0.5969          | 0.8480   | 0.8480      | 0.8480   | 0.7939   | 0.8480          | 0.8480       | 0.7918       | 0.8481             | 0.8480          | 0.7961          |
| 0.0253        | 10.0  | 1830 | 0.5560          | 0.8549   | 0.8526      | 0.8549   | 0.8024   | 0.8549          | 0.8549       | 0.8016       | 0.8527             | 0.8549          | 0.8073          |
| 0.0204        | 11.0  | 2013 | 0.5697          | 0.8522   | 0.8494      | 0.8522   | 0.7970   | 0.8522          | 0.8522       | 0.7840       | 0.8482             | 0.8522          | 0.8122          |
| 0.019         | 12.0  | 2196 | 0.5809          | 0.8522   | 0.8507      | 0.8522   | 0.8007   | 0.8522          | 0.8522       | 0.8006       | 0.8503             | 0.8522          | 0.8025          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-Twitter_Sentiment_Analysis_v2", "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 

bert-base-uncased-Twitter\_Sentiment\_Analysis\_v2
==================================================


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


* Loss: 0.5809
* Accuracy: 0.8522
* Weighted f1: 0.8507
* Micro f1: 0.8522
* Macro f1: 0.8007
* Weighted recall: 0.8522
* Micro recall: 0.8522
* Macro recall: 0.8006
* Weighted precision: 0.8503
* Micro precision: 0.8522
* Macro precision: 0.8025


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1