



# BioBart_Large_Dialouge_Summarization_taskA

This model is a fine-tuned version of [GanjinZero/biobart-large](https://https://huggingface.co/GanjinZero/biobart-large) on the [Task A-Short Dialogue2Note Summarization dataset](https://github.com/abachaa/MEDIQA-Chat-2023)

It achieves the following results on the evaluation set:
- Loss: 2.7487
- Rouge1: 28.7953
- Rouge2: 13.7224
- Rougel: 27.8491
- Rougelsum: 28.6028
- Gen Len: 19.34

## 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: 8
- eval_batch_size: 8
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log	    | 1.0   | 1842 | 2.739522        | 29.7742 | 15.4000 | 29.0754 | 29.706200 | 19.5600 |
| No log	    | 2.0   | 3684 | 2.705775	     | 28.6469 | 13.7595 | 27.7582 | 28.486700 | 19.3700 |
| No log	    | 3.0   | 5526 | 2.748785	     | 28.7953 | 13.7224 | 27.8491 | 28.602800 | 19.3400 |



### Framework versions

- Transformers 4.26.1
- datasets 2.10.1
- tokenizers 0.13.2
{"license": "apache-2.0", "datasets": ["MEDIQA-Chat-2023-taskA"], "metrics": ["rouge"], "model-index": [{"name": "BioBart_Large_Dialouge_Summarization_taskA", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "MEDIQA-Chat-2023-taskA", "type": "MEDIQA-Chat-2023-taskA", "config": "MEDIQA-Chat-2023-taskA", "split": "train", "args": "MEDIQA-Chat-2023-taskA"}, "metrics": [{"type": "rouge", "value": 28.7953, "name": "Rouge1"}]}]}]}
transformers pytorch bart text2text-generation dataset:MEDIQA-Chat-2023-taskA license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #dataset-MEDIQA-Chat-2023-taskA #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

BioBart\_Large\_Dialouge\_Summarization\_taskA
==============================================


This model is a fine-tuned version of GanjinZero/biobart-large on the Task A-Short Dialogue2Note Summarization dataset


It achieves the following results on the evaluation set:


* Loss: 2.7487
* Rouge1: 28.7953
* Rouge2: 13.7224
* Rougel: 27.8491
* Rougelsum: 28.6028
* Gen Len: 19.34


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: 8
* eval\_batch\_size: 8
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.26.1
* datasets 2.10.1
* tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* datasets 2.10.1
* tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #dataset-MEDIQA-Chat-2023-taskA #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* datasets 2.10.1
* tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #dataset-MEDIQA-Chat-2023-taskA #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.26.1
* datasets 2.10.1
* tokenizers 0.13.2