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# Hypert-medical

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.

## 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: 36
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 216
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 10000

### Training results



### Framework versions

- Transformers 4.21.0
- Pytorch 1.10.1+cu111
- Datasets 2.10.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Hypernymy-Aware-BERT-Medical-v4", "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 




# Hypert-medical

This model is a fine-tuned version of bert-base-uncased on an unknown dataset.

## 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: 36
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 216
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 10000

### Training results



### Framework versions

- Transformers 4.21.0
- Pytorch 1.10.1+cu111
- Datasets 2.10.1
- Tokenizers 0.12.1

# Hypert-medical

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. ## 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: 36
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 216
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 10000 ### Training results ### Framework versions

- Transformers 4.21.0
- Pytorch 1.10.1+cu111
- Datasets 2.10.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Hypert-medical

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. ## 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: 36
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 216
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 10000 ### Training results ### Framework versions

- Transformers 4.21.0
- Pytorch 1.10.1+cu111
- Datasets 2.10.1
- Tokenizers 0.12.1