
# selims

This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the tweet_eval dataset.

## Model description

This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews.

## Intended uses & limitations

This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. 
## Training and evaluation data
For fine-tunning of this model, the Tweet_eval dataset was used.

## Training procedure
Please refer to the information below:

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3


{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "widget": [{"text": "I love conducting research on twins!", "example_title": "Sentiment analysis - English"}, {"text": "Ja, ik vind het tweelingen onderzoek leuk maar complex, weet je.", "example_title": "Sentiment analysis - Dutch"}], "model-index": [{"name": "selims", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:tweet_eval license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tweet_eval #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# selims

This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the tweet_eval dataset.

## Model description

This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews.

## Intended uses & limitations

This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. 
## Training and evaluation data
For fine-tunning of this model, the Tweet_eval dataset was used.

## Training procedure
Please refer to the information below:

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3


# selims

This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the tweet_eval dataset. ## Model description

This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews. ## Intended uses & limitations

This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. ## Training and evaluation data
For fine-tunning of this model, the Tweet_eval dataset was used. ## Training procedure
Please refer to the information below: ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tweet_eval #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # selims

This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the tweet_eval dataset. ## Model description

This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews. ## Intended uses & limitations

This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. ## Training and evaluation data
For fine-tunning of this model, the Tweet_eval dataset was used. ## Training procedure
Please refer to the information below: ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-tweet_eval #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# selims

This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the tweet_eval dataset.## Model description

This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews.## Intended uses & limitations

This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian.## Training and evaluation data
For fine-tunning of this model, the Tweet_eval dataset was used.## Training procedure
Please refer to the information below:### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3