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# finetuning-sentiment-text-mining


- Loss: 1.3600
- Accuracy: 0.784
- F1: 0.7840

## Model description

This model was finetuned for course Text mining for Ai at Vrije university Amsterdam

## Intended uses & limitations
Only use is for the final project in the course 

## Training and evaluation data

Data were reddit and twitter comments or tweets

## 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: 10

### Training results



### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-text-mining", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# finetuning-sentiment-text-mining


- Loss: 1.3600
- Accuracy: 0.784
- F1: 0.7840

## Model description

This model was finetuned for course Text mining for Ai at Vrije university Amsterdam

## Intended uses & limitations
Only use is for the final project in the course 

## Training and evaluation data

Data were reddit and twitter comments or tweets

## 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: 10

### Training results



### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
# finetuning-sentiment-text-mining


- Loss: 1.3600
- Accuracy: 0.784
- F1: 0.7840 ## Model description

This model was finetuned for course Text mining for Ai at Vrije university Amsterdam ## Intended uses & limitations
Only use is for the final project in the course ## Training and evaluation data

Data were reddit and twitter comments or tweets ## 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: 10 ### Training results ### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # finetuning-sentiment-text-mining


- Loss: 1.3600
- Accuracy: 0.784
- F1: 0.7840 ## Model description

This model was finetuned for course Text mining for Ai at Vrije university Amsterdam ## Intended uses & limitations
Only use is for the final project in the course ## Training and evaluation data

Data were reddit and twitter comments or tweets ## 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: 10 ### Training results ### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# finetuning-sentiment-text-mining


- Loss: 1.3600
- Accuracy: 0.784
- F1: 0.7840## Model description

This model was finetuned for course Text mining for Ai at Vrije university Amsterdam## Intended uses & limitations
Only use is for the final project in the course## Training and evaluation data

Data were reddit and twitter comments or tweets## 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: 10### Training results### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2