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# nlp_summarization_project

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2856
- Rouge1: 28.5817
- Rouge2: 9.6715
- Rougel: 20.5872
- Rougelsum: 21.6921
- Gen Len: 65.425

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "nlp_summarization_project", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "config": "default", "split": "train[:2000]", "args": "default"}, "metrics": [{"type": "rouge", "value": 28.5817, "name": "Rouge1"}]}]}]}
transformers pytorch bart text2text-generation generated_from_trainer dataset:xsum license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-xsum #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# nlp_summarization_project

This model is a fine-tuned version of facebook/bart-large-cnn on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2856
- Rouge1: 28.5817
- Rouge2: 9.6715
- Rougel: 20.5872
- Rougelsum: 21.6921
- Gen Len: 65.425

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# nlp_summarization_project

This model is a fine-tuned version of facebook/bart-large-cnn on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2856
- Rouge1: 28.5817
- Rouge2: 9.6715
- Rougel: 20.5872
- Rougelsum: 21.6921
- Gen Len: 65.425 ## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-xsum #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # nlp_summarization_project

This model is a fine-tuned version of facebook/bart-large-cnn on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2856
- Rouge1: 28.5817
- Rouge2: 9.6715
- Rougel: 20.5872
- Rougelsum: 21.6921
- Gen Len: 65.425 ## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3