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# Neo-VN-2.7B

Work-in-progress. Current version is very bad.

This model is a fine-tuned version of [huggingface.co/EleutherAI/opt-neo-2.7b](https://huggingface.co//notebooks/convogpt/gpt-neo-2.7B/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4748
- Accuracy: 0.4671

## Model description

Neo-VN-2.7B is a transformer model based on EleutherAI's replication of GPT-3.


## Training procedure

Fine-tuned on 233MB of conversational data.


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 2.3941        | 1  | 20460 | 0.4671   | 2.4748          |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "2.7b", "results": []}]}
transformers pytorch tensorboard gpt_neo text-generation generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

Neo-VN-2.7B
===========


Work-in-progress. Current version is very bad.


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


* Loss: 2.4748
* Accuracy: 0.4671


Model description
-----------------


Neo-VN-2.7B is a transformer model based on EleutherAI's replication of GPT-3.


Training procedure
------------------


Fine-tuned on 233MB of conversational data.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1