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# ec-biogpt-noised-pubmed-v4

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8204

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7942        | 0.11  | 500   | 1.8358          |
| 1.9793        | 0.21  | 1000  | 1.8000          |
| 1.9244        | 0.32  | 1500  | 1.7763          |
| 1.8871        | 0.43  | 2000  | 1.7623          |
| 1.6525        | 0.54  | 2500  | 1.7511          |
| 1.6871        | 0.64  | 3000  | 1.7401          |
| 1.5771        | 0.75  | 3500  | 1.7315          |
| 1.732         | 0.86  | 4000  | 1.7278          |
| 1.9909        | 0.96  | 4500  | 1.7196          |
| 1.5173        | 1.07  | 5000  | 1.7204          |
| 1.6015        | 1.18  | 5500  | 1.7206          |
| 1.6817        | 1.28  | 6000  | 1.7183          |
| 1.6475        | 1.39  | 6500  | 1.7161          |
| 1.7425        | 1.5   | 7000  | 1.7114          |
| 1.4702        | 1.61  | 7500  | 1.7067          |
| 1.5635        | 1.71  | 8000  | 1.7078          |
| 1.574         | 1.82  | 8500  | 1.7020          |
| 1.6691        | 1.93  | 9000  | 1.6985          |
| 1.4796        | 2.03  | 9500  | 1.7339          |
| 1.472         | 2.14  | 10000 | 1.7354          |
| 1.4476        | 2.25  | 10500 | 1.7331          |
| 1.4402        | 2.35  | 11000 | 1.7327          |
| 1.5988        | 2.46  | 11500 | 1.7328          |
| 1.3682        | 2.57  | 12000 | 1.7299          |
| 1.4988        | 2.68  | 12500 | 1.7281          |
| 1.4514        | 2.78  | 13000 | 1.7257          |
| 1.6356        | 2.89  | 13500 | 1.7264          |
| 1.6653        | 3.0   | 14000 | 1.7240          |
| 1.2013        | 3.1   | 14500 | 1.7782          |
| 1.2864        | 3.21  | 15000 | 1.7770          |
| 1.4638        | 3.32  | 15500 | 1.7817          |
| 1.2501        | 3.43  | 16000 | 1.7787          |
| 1.4613        | 3.53  | 16500 | 1.7791          |
| 1.1816        | 3.64  | 17000 | 1.7767          |
| 1.1841        | 3.75  | 17500 | 1.7786          |
| 1.2382        | 3.85  | 18000 | 1.7743          |
| 1.2868        | 3.96  | 18500 | 1.7749          |
| 1.2074        | 4.07  | 19000 | 1.8167          |
| 1.1657        | 4.17  | 19500 | 1.8224          |
| 1.1851        | 4.28  | 20000 | 1.8197          |
| 1.1141        | 4.39  | 20500 | 1.8225          |
| 1.0628        | 4.5   | 21000 | 1.8202          |
| 1.0946        | 4.6   | 21500 | 1.8209          |
| 1.037         | 4.71  | 22000 | 1.8201          |
| 1.1277        | 4.82  | 22500 | 1.8206          |
| 1.2766        | 4.92  | 23000 | 1.8204          |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "ec-biogpt-noised-pubmed-v4", "results": []}]}
transformers pytorch biogpt text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #biogpt #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

ec-biogpt-noised-pubmed-v4
==========================


This model is a fine-tuned version of microsoft/biogpt on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8204


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #biogpt #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #biogpt #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3