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# led-large-16384-finetuned-big_patent

This model is a fine-tuned version of [robingeibel/led-large-16384-finetuned-big_patent](https://huggingface.co/robingeibel/led-large-16384-finetuned-big_patent) on an unknown dataset.
It achieves the following results on the evaluation set:


## 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:
- optimizer: None
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "led-large-16384-finetuned-big_patent", "results": []}]}
transformers pytorch tf tensorboard led feature-extraction generated_from_keras_callback license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us 




# led-large-16384-finetuned-big_patent

This model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.
It achieves the following results on the evaluation set:


## 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:
- optimizer: None
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1

# led-large-16384-finetuned-big_patent

This model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.
It achieves the following results on the evaluation set: ## 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:
- optimizer: None
- training_precision: float32 ### Training results ### Framework versions

- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us 
 # led-large-16384-finetuned-big_patent

This model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.
It achieves the following results on the evaluation set: ## 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:
- optimizer: None
- training_precision: float32 ### Training results ### Framework versions

- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us 
# led-large-16384-finetuned-big_patent

This model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.
It achieves the following results on the evaluation set:## 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:
- optimizer: None
- training_precision: float32### Training results### Framework versions

- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1