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# bert-hateful-memes-expanded

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on texts from the following datasets:
- [Hateful Memes](https://hatefulmemeschallenge.com/), `train`, `dev_seen` and `dev_unseen`
- [HarMeme](https://github.com/di-dimitrov/harmeme), `train`, `val` and `test`
- [MultiOFF](https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text), `Training`, `Validation` and `Testing`

It achieves the following results on the evaluation set:
- Loss: 3.7600

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.11.0
- Pytorch 1.8.1+cu102
- Datasets 1.8.0
- Tokenizers 0.10.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-hateful-memes-expanded", "results": []}]}
transformers pytorch bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# bert-hateful-memes-expanded

This model is a fine-tuned version of bert-base-uncased on texts from the following datasets:
- Hateful Memes, 'train', 'dev_seen' and 'dev_unseen'
- HarMeme, 'train', 'val' and 'test'
- MultiOFF, 'Training', 'Validation' and 'Testing'

It achieves the following results on the evaluation set:
- Loss: 3.7600

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.11.0
- Pytorch 1.8.1+cu102
- Datasets 1.8.0
- Tokenizers 0.10.2

# bert-hateful-memes-expanded

This model is a fine-tuned version of bert-base-uncased on texts from the following datasets:
- Hateful Memes, 'train', 'dev_seen' and 'dev_unseen'
- HarMeme, 'train', 'val' and 'test'
- MultiOFF, 'Training', 'Validation' and 'Testing'

It achieves the following results on the evaluation set:
- Loss: 3.7600 ## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.11.0
- Pytorch 1.8.1+cu102
- Datasets 1.8.0
- Tokenizers 0.10.2
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert-hateful-memes-expanded

This model is a fine-tuned version of bert-base-uncased on texts from the following datasets:
- Hateful Memes, 'train', 'dev_seen' and 'dev_unseen'
- HarMeme, 'train', 'val' and 'test'
- MultiOFF, 'Training', 'Validation' and 'Testing'

It achieves the following results on the evaluation set:
- Loss: 3.7600 ## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.11.0
- Pytorch 1.8.1+cu102
- Datasets 1.8.0
- Tokenizers 0.10.2
passage: TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert-hateful-memes-expanded

This model is a fine-tuned version of bert-base-uncased on texts from the following datasets:
- Hateful Memes, 'train', 'dev_seen' and 'dev_unseen'
- HarMeme, 'train', 'val' and 'test'
- MultiOFF, 'Training', 'Validation' and 'Testing'

It achieves the following results on the evaluation set:
- Loss: 3.7600## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Training results### Framework versions

- Transformers 4.11.0
- Pytorch 1.8.1+cu102
- Datasets 1.8.0
- Tokenizers 0.10.2