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# finetuning-sentiment-all_df

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5500
- Accuracy: 0.7683
- F1: 0.7683

## 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: 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: 2

### Training results



### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
{"language": ["en", "th"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue", "wisesight_sentiment", "tweet_eval", "imdb"], "metrics": ["accuracy", "f1"], "pipeline_tag": "text-classification", "widget": [{"text": "\u0e44\u0e1b\u0e40\u0e25\u0e22\u0e22\u0e22\u0e22 555 \u0e42\u0e04\u0e15\u0e23\u0e04\u0e38\u0e49\u0e21\u0e21\u0e21\u0e21\u0e04\u0e48\u0e32", "example": "pos"}, {"text": "\u0e22\u0e32\u0e1a\u0e49\u0e32\u0e42\u0e17\u0e29\u0e1b\u0e23\u0e30\u0e2b\u0e32\u0e23\u0e19\u0e30\u0e04\u0e23\u0e31\u0e1a", "example": "neu"}, {"text": "\u0e08\u0e31\u0e1a\u0e1a\u0e38\u0e2b\u0e23\u0e35\u0e48\u0e44\u0e1f\u0e1f\u0e49\u0e32 \u0e22\u0e36\u0e14\u0e23\u0e16????", "example": "neg"}], "model-index": [{"name": "finetuning-sentiment-all_df", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer en th dataset:glue dataset:wisesight_sentiment dataset:tweet_eval dataset:imdb license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #th #dataset-glue #dataset-wisesight_sentiment #dataset-tweet_eval #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 




# finetuning-sentiment-all_df

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5500
- Accuracy: 0.7683
- F1: 0.7683

## 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: 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: 2

### Training results



### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
# finetuning-sentiment-all_df

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5500
- Accuracy: 0.7683
- F1: 0.7683 ## 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: 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: 2 ### Training results ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #th #dataset-glue #dataset-wisesight_sentiment #dataset-tweet_eval #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # finetuning-sentiment-all_df

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5500
- Accuracy: 0.7683
- F1: 0.7683 ## 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: 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: 2 ### Training results ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #th #dataset-glue #dataset-wisesight_sentiment #dataset-tweet_eval #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# finetuning-sentiment-all_df

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5500
- Accuracy: 0.7683
- F1: 0.7683## 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: 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: 2### Training results### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2