
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# esm2_t12_35M_UR50D-finetuned-localization

This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2355
- F1: 0.6929

## 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: 1e-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
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4654        | 1.0   | 3889  | 0.4648          | 0.6902 |
| 0.4139        | 2.0   | 7778  | 0.4605          | 0.7270 |
| 0.3621        | 3.0   | 11667 | 0.4892          | 0.7199 |
| 0.2574        | 4.0   | 15556 | 0.6158          | 0.6910 |
| 0.1731        | 5.0   | 19445 | 0.8932          | 0.6925 |
| 0.1162        | 6.0   | 23334 | 1.4826          | 0.6712 |
| 0.0651        | 7.0   | 27223 | 1.8881          | 0.6937 |
| 0.0565        | 8.0   | 31112 | 2.1033          | 0.6944 |
| 0.0319        | 9.0   | 35001 | 2.2087          | 0.6909 |
| 0.0153        | 10.0  | 38890 | 2.2355          | 0.6929 |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "esm2_t12_35M_UR50D-finetuned-localization", "results": []}]}
transformers pytorch tensorboard esm text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #esm #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

esm2\_t12\_35M\_UR50D-finetuned-localization
============================================


This model is a fine-tuned version of facebook/esm2\_t12\_35M\_UR50D on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.2355
* F1: 0.6929


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: 1e-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
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #esm #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3